From d77224729d653040938189b984c487e27c23ad7d Mon Sep 17 00:00:00 2001 From: Stone Tao Date: Fri, 26 Apr 2024 15:58:46 -0700 Subject: [PATCH] Update quadruped envs and procedural generated floor texture (#279) * v0.6.0 Alpha Release: Sapien 3 integration (#175) * version bump * copy code over * refactor xmate3 * Update xmate3.py * Changed Replay Trajectory to add options to record rewards as well as specify reward mode (#177) * Added reward mode argument and record rewards argument to replay trajectory * Fixed an error --------- Co-authored-by: Arnav G Co-authored-by: Stone Tao * merge in refactor and updates for utils * merge in changes for trajectory * merge new examples * merge new env code, refactors + new scene builder api * merge new agents code * merge code for new assets, floors etc. * Update __init__.py * Delete README.md --------- Co-authored-by: Arnav G <73305591+arnavg115@users.noreply.github.com> Co-authored-by: Arnav G * bump version and remove old requirements.txt file * merge updates (#183) - fix bug where some files were missing for franka for motion planning code - fix bug that required user to have ai2 assets - update floor tiles * fix bug with scene demos * version bump * Update README.md * merge updates * Added decision transformer implementation to baseline (#180) * copy over ms3 code * fix pick cube task reward * code format * fix bug with to_tensor * fix bug with to_tensor calls * Fetch mobile (#188) * Progress: Fetch mobile base * fix * fix * unfinished: integrate into gpu sim stuff * Fix: gpu sim compatibility issues * Fix: fetch sensor cfg * Fix: pd_ee_pose controller ik, env step cpu sim output, utils, fetch pose * Fix: fetch is_grasping cpu sim * code formatting * fix a few a number of environments and tests (#190) * fix a few envs, mark pick clutter as to be fixed in the future. * code refactor * fix some batching code, only reset and step return numpy on cpu mode * format code * code formatting * remove a lot of unused variables * gpu end effector control + various bug fixes (#191) * update template * add minimal pick cube task with "task sheet" * task sheets * init * fix some bugs * auto batch actions before processing * init * work * bug fix * Update visual_all_envs_cpu.py * code golfing * Update sapien_env.py * update some tests * Update vec_env.py * formatting * Squashed commit of the following: commit 27101d4bdc4bd98451ed38d209045b36794288a9 Author: Stone Tao Date: Tue Jan 23 18:12:03 2024 -0800 fix some bugs with fetch * fix bug with ee pose controller for panda * Fix: pd_base_vel action expected Float not Double * Trivial: table scene builder height and width * Tasks remake for GPU sim (#192) * work * sac impl * update template * add minimal pick cube task with "task sheet" * task sheets * work * work * work * fix templates * using torch rng * use torch rng * batched uniform sampler, stack cube v1 task * fix bugs with scene masking, merge_actors function * Update pick_single_ycb.py * Update pick_single_ycb.py * bug fixes * stack cube task bug fix * refactor, appending hidden_objects list allows handling of hiding e.g. goal sites * WIP work * Revert "WIP work" This reverts commit 9a6710f45b2eb03df634e1bc979eb0cd74378ccc. * quick fix: table dims in scene builder * Tasks remake (#195) * work * sac impl * update template * add minimal pick cube task with "task sheet" * task sheets * work * work * work * fix templates * using torch rng * use torch rng * batched uniform sampler, stack cube v1 task * fix bugs with scene masking, merge_actors function * Update pick_single_ycb.py * Update pick_single_ycb.py * bug fixes * stack cube task bug fix * refactor, appending hidden_objects list allows handling of hiding e.g. goal sites * WIP work * Revert "WIP work" This reverts commit 9a6710f45b2eb03df634e1bc979eb0cd74378ccc. * Revert "Revert "WIP work"" This reverts commit dcb3a5f6524c8abb9c790174339715aa3929f7a7. * refactor some code, cache some properties, batch some properties * fix naming of joints and links to also be prepended by scene-{i} * bug fixes. Disable some functionality via a ._merged bool * disable hand camera on panda (people should use the real sense panda config * fix some setters * work * cache property for body data indices, it is now auto computed for user * work * change default value for static as resting objects still have a minor amount of angular rotation at times * code updates * decorator to assert error when gpu is initialized for calling functions user should not call. * Update actor.py * bug fixes * work * new utility functions for getting desired meshes, fixed goal site place * performance improvements, swap obs and info * Update __init__.py * Update articulation.py * Update pick_single_ycb.py * update documentation * tuned max episode steps for faster PPO * use memory leak free new sapien and new buffer access api, fix controller reconfigure * bug fixes, new tests * test * more bug fixes * work * add velocity target control gpu support. perf improvements for indexing articulation related things. Fix some bugs * Update base_controller.py * fix collision setting bugs * apply vel * fix self collision bugs * fix gymnasium vector env wrapper to ignore the timelimit wrapper * clean up * fixes * linkmerging * bug fix * work * better camera angles * work * bug fixes * code clean up with new wrappers * Fix: pick cube get_obs_extra pass info * Fix: fetch is_static * Benchmarking code (#198) * cleaner benchmarking code * code init * Update benchmark_cpu_sim.py * Update benchmark_gpu_sim.py * basic profiler * update benchmark code * code update * Update README.md * Fix: fetch is_static --------- Co-authored-by: arth-shukla <44527268+arth-shukla@users.noreply.github.com> * Code cleanup (#197) * cleanup * remove old tasks * cleanup * refactor * work * more tests * fix tests * visual-encoders (#199) * First commit of VisualEncoder Add necessary files * Try to deal with ConnectionResetError by changing wrappers location * Change wrappers * Removed tyro * Change filename to lowercase * work and add tests * Delete ppo_liftcube_VisualEncoder.py * fixes --------- Co-authored-by: itsygao Co-authored-by: ygaoproj <102192613+ygaoproj@users.noreply.github.com> * faster benchmark results, new sapien * bug fix * Fix build_actor_ai2 Actor * fix bug in cpu sim with init_raw_obs * refactor some names from render_cameras to render_human_cameras and cameras to sensors * Fix: fetch init * Fix: fetch action space normed to -1 to 1 * Expand sensor api properly and refactor code to use it (#201) * new tests, mark gpu tests, rework sensor api * renaming * new test * code clean up * docs * Update sapien_env.py * refator more code. Observation processing code only delete sensor data that they replace now. * bug fix with human renderer * fetch torso link * Multirobot support + basic tasks (#202) * multi robot support * work * Update two_robot_stack_cube.py * finish task obs and reward * reward, fixes * bug fixes * refinement * Update pick_cube.py * better camera angles * fix int issue * work * Update two_robot_stack_cube.py * work * robot support typing * better debugging camera * Update two_robot_stack_cube.py * work * Update ppo.py * add multi agent task tests * bug fix with SUPPORTED_ROBOTS attribute * Save benchmark data to csv (#203) * profiling code to csv * Update benchmark_gpu_sim.py * trivial pr: `cameras` render mode (#204) * Trivial: add cameras, renders both human cameras and agent sensors * Fix: cpu inverted colors bug * Remove cameras render mode * Reset by index (#205) * support partial resets and add test * adhere to gymnasium * support fail and success terminations and update docs * work * Update link.py * Settable default scene/memory configs + attempt at legged tasks + bug fixes (#206) * basic locomotion robot * work * bug fixes * work * work * Update articulation.py * bug fixes * tuned sim configs for some tasks * tuned scene configs for more tasks * work * Update benchmark_gpu_sim.py * Update sapien_env.py * bug fix with controller not resetting after each env reset, affecting e.g. joint delta pos controllers (#207) * fix controller not resetting to a initial state when env.reset is called... * Update pd_joint_pos.py * simplify code * fixes * use a lower memory configuration for all gpu sim tests * reset agent before episode initialization in case episode initialization will set other qpos/qvel/qf values * Update minimal_template.py * fetch reachable dist * update pick cube mp solution * work * init code * Update interactive_teleop.py * fix rt rendering * Fixed bug for resetting elapsed steps (#211) * fix some bugs with interactive panda tele-op and recording epsiodes and state get/set (#210) * fix state setting. * Update record.py * work * Update record.py * wip * buyg fixes and work * bug fixes * bug fixes * work * Update interactive_teleop.py * Update pick_single_ycb.py * reduce verbosity * add batched timelimit wrapper only for gpu sim envs. Otherwise use gymnasium CPU one. * Update registration.py * use make_vec api? and avoid timelimit wrapper type issues * bug fixes * fix tests * Update test_gpu_envs.py * bug fixes * Update ppo.py * bug fixes * fix bug where gymnasium vecenv wrapper would mess up success indicators and now gymnasium vecenv wrapper will auto find max episode steps value * Fix: clone() on int in cpu sim * Net force (#213) * new package * add net force tensor for a body * net forces for multiple links on articulation * work * Update README.md * Update xmate3.py * update sapien version * bug fixes with gpu ppo + partial resets (#215) * partial reset + rookie implementations * Update ppo.py * Update README.md * New task: DClaw Valve Rotating (#214) * add: add dclaw rotating task * ref: resolve todo * fix: fix partial reset bug and make the environment much simpler for level 0 * work (#216) * Fix: update render_human camera pos each render (e.g. when mounted to moving object) * Baselines diffusion policy (#196) * init diff policy baseline * added training script and readme * Finished diffusion policy baseline * corrected jupyter notebook * formatted notebook * lowercase --------- Co-authored-by: Stone Tao * fix bug where viewer camera was fixed in place to a sensor * fix bug where failure is not used for termination * bug fix with adding mounted cameras * GPU Simulated Scenes, New more organized asset locations (#217) * Fix: some scene manip env issues * Better fetch cameras * Fix: disable collisions only on fetch wheels * Fix: fetch grapsing retval * Progress: updates to scene-related classes/envs * Fix: ai2thor scene builder set poses in initialize() * Fix: ai2thor fetch scene builder ground collisions * Fix: ai2 sb don't reconfigure by default * Fix: sb load_actors instead of load_agents * Trivial: add fetch resting qpos as attr * Fix: pick_object scene pose setting * Fix: renderer follows camera * Fix: options is None * Fix: fetch body delta pos controller * Fix: record wrapper options=None by default * Progress: support for loading navigable positions * Progress: update base scene builder w/ DexM3 version * Trivial: add arm camera, more "working" coacd objects * minor ai2 sb changes * Trivial: port over everything needed for scene manip testing * bigger place reward * Fix: keep x/y/zrot qvel in obs * Try tune reward * scale place reward more * Try lessen place rew * work * work * add bug fix * Create scene_gpu.py * Update sequential_task.py * temp code * make more objects static * base empty scene manipulation env * work * work * tests * work * more work * modify benchmark code to support cpu and gpu sim tests * add lighting? * work * articulations * work * Update scene_builder.py * new dataset location * fix for new dataset paths for some tasks, remove old tasks * remove scene tasks as they are not done * work * work * fixes * change some defaults --------- Co-authored-by: arth-shukla <44527268+arth-shukla@users.noreply.github.com> * Fix actor show/hide visual gpu sim (#221) * Fix: actor show/hide visual not working on gpu sim * Fix: by default hidden poses hidden * bug fix * Fix: setter and getter for actor.pose returns before_hide_pose if hidden, Fix: render human also sets _hidden_objs to hidden when doen rendering * Unit test + fix for hide/show visual --------- Co-authored-by: StoneT2000 * New docs (#220) * work * Update setup.py * lots of docs * work * new theme and work * readthedocs * work * bug fix * fix * work * new task: in hand rotating in_hand_task (#219) * fix bug where articulation get net contact forces was oriented wrong despite having right shape * Fix: fetch in TableSceneBuilder * New trajectory format and bug fixes (#222) * work * work? * bug fixes and support new state dict format, reduce robot name verbosity * Update pick_cube.py * Update test_gpu_envs.py * Update test_gpu_envs.py * bug fix with setting before hide poses * bug fixes * work * work * fix a bunch of bugs * fix batching for scalar values * Update benchmark_maniskill.py * bug fix * add more useful print for ppo * fix bug where controller reset did nothandle partial reset correctly and new tests * bug fixes * bug fix with set state dict where merged actor didnt have the right new name and other bugs * bug fix to handle both single and multiple envs * bug fixes with formatting * Update record.py * sapien upgrade * Update sapien_utils.py * work * fix default value of gravity issue caused by newer python verisons * Robot and scene builder registration and all env testing and temporarily removing old envs (#224) * work * unregister envs that are not meant to be run by themselves * bug fixes * bug fixes * Update record.py * Update README.md * remove old code, register another scene * typings * make scene builder api consistent * Update table_scene_builder.py * Packaging and import fixing (#225) * add lots of init py files * new imports 1 * more * work * work * work * fixes * Update setup.py * remove circular import errors? * support a default sim configuration choice * use dacite package to fix nested dataclasses and typecheck the sim configs * Update sapien_env.py * More ms3 docs (#227) * use shared version in docs and pip * work * support upload videos in docs * work * Update .gitignore * docs * Update teleoperation.md * work * work * docs, move some code around * Update quickstart.md * some benchmarking results * new theme with better github support * examples * Update teleoperation.md * docs * work * Update custom_tasks.md * work * Update sapien_env.py * work * more docs * work * fix rtd * Update conf.py * temp fix for videos on rtc * New packaging (#228) * work * www * updates * Update setup.py * renaming a lot * work * Update publish-to-pypi.yml * Bug fixes releases (#229) * work * more docs, change far for cameras * Fix: circular import from table scene builder * Mp work (#230) * motion planning demos * work * Update motionplanning.md * work * work * work * examples * Update interactive_panda.py * remove old TODOs * Update setup.py * Update types.py * docs * Datasets+replay fix gpu camera matrices (#231) * Update index.md * raise user warning about registering with types * Update README.md * Update README.md * disable target ee control mode due to bug atm, fix up replay trajectories * clean up code * www * Update observations.py * rgb option and work and bug fixes * lots of bug fixes * docs, small optimization to cache matrices * work * dataset documentation and some refactoring * nice documentation and figures and bug fix * Update panda.py * more documentation on advanced featureas * more docs * typos * more typo fixes * code refactoring to simplify maniskill lifecycle (#232) * refactor: reconfigure -> _reconfigure. Add _after_reconfigure() function for use cases like mobile manip. pick task * refactor: _load_actors -> _load_scene. Deprecate _load_articulations in favor of user self organization in their code * refactor _setup_lighting -> _load_lighting. better docs * Update sapien_env.py * docs * multiple refactors (#233) * refactoring * bug fixes * refactor: _register_* to _sensor_configs property * refactoring default sim cfg * consistency work * consistent namings * bump and include .stl files for mplib * bug fixes * Update setup.py * Pull cube and Lift peg tasks with demo videos (#234) * pull cube and lift peg tasks * pull cube and lift peg tasks * More docs (#235) * Update README.md * refactoring * refactor the new tasks * fix tasks, add more docs on tasks * new diagrams and work * temporarily remove not up to date code * Update installation.md * Update Dockerfile * fix typos in docs * new sapien package without mem leak * More demos more docs (#236) * Create contact_pair_impulse_test.py * fixes * Update setup.py * work * work * Update download_asset.py * bug fix with table scene builder * Update demo_vis_pcd.py * fix bug where deepcopy will track internal dict references that broke parse urdf config * Add back some old tasks and bug fixes (#237) * bug fix, upgrade realsense panda arm with actual real sense camera mesh * work * bug fix for episode recording * bug fixes, reward mode none supported * version bump fixes: - extrinsic camera matrix was None, broke some visual observations - improved panda + camera model - bug where gpu sim used with 1 parallel env has broken visual data processing code - bug where save video did not save separate videos on CPU sim. changes: - add back peg insertion side task (but without dense reward as its not easy to batch) * Update observations.py * Update installation.md * fix demo name * AssemblingKits-v1 on gpu, update docs and more consistent video names (#239) * work * use scene_idxs as opposed to scene_mask for faster loading, for loops are too slow * Update assembling_kits.py * bug fixes * work * updated documentation on scene_idxs (faster than scene_masks) * partially support mimic joints (#238) * partially support mimic joints * Update articulation_builder.py --------- Co-authored-by: StoneT2000 * fix warning consistency * camera randomization tutorial/vectorized camera poses and some refactoring (#240) * vectorized camera poses and refactor camera config * bug fixes * fixes * documentation * Update domain_randomization.md * Update domain_randomization.md * better naming * refactoring some code * a * Refactor scene mask to scene idxs (#242) * move out scene masks in favor of scene idxs * Update link.py * Update custom_tasks_advanced.md * bug fix where could not mount camera on actor and docs * dev4 version bump + fix bug with human render camera global pose * typo * work * fix typos * Refactor to provide options in load and initialize related functions (#243) * work * Update custom_tasks.md * update documentation for new features, evaluate and get obs extra are optional now * Added ppo partial reset arg (#241) * Added ppo-partial reset arg * Changed eval envs to also have a partial reset arg * Fixed bug * require actors/articulations built to have unique names * Task tuning (#244) * tuning * Update README.md * Update assembling_kits.py * Update ppo.py * update docs to clarify the gymnasium wrapper implementation reasons * Update setup.py * fix bug with pcd demo * Fix: replicacad static obj poses * Fix: replicacad pose issues * Use fast kinematics library now for IK * moving demos to hugging face and small change in demo storage structure. * Created using Colaboratory * Update setup.py * move tutorial to right location * fix demo manual control, hardcoded for fetch for now, and fix render functions returning batched images when there is only one environment * support (batched) intrinsics in camera config and bug fix with demo manual control * bug fix with ordering in dataclass * set some defaults for camera cfg and fix ordering bug for some tasks * Update setup.py * Update README.md * bug fix with typings for python 3.11 * update docs * Batched drives, hide not ready functions, improved typing of structs, some bug fixes for unregistered envs (#248) * support using different drive mode in the joint position controller * refactoring * work * w * w * fix typings * w * w * version bump * Created using Colaboratory * move draft tutorial * docs on how to contribute tasks (#250) * add * w * docs * work * work * Rotate Cube Task using Trifingerpro robot (#249) * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * Fix pd ee pose for non panda robots, name refactors to be more explicit, and links have references to their joints (#253) * save joint reference in links * some code refactor, gpu sim ee control for other robots should be working now * Pick clutter task (GPU) (#254) * init * Update pick_clutter_ycb.py * fix bug with reset mask * support more indexing options for Pose, actor_views and articulation_views, pick clutter task work, bug fix with pd_ee_pose controller * return in reset info whether we reconfigure, necessary to know in order to determine when to delete trajectory buffer in record wrapper * Update actor.py * Update sapien_env.py * Update pick_clutter_ycb.py * docs * Update pick_clutter_ycb.py * fix bug with rotate cube task requiring pytorch kinematics library * Update articulation.py * Fix doc typos and refactor some old links/names (#255) * updates to docs * refactorings * remove old code and fix circular import bugs with register envs utility * sapien.physx -> physx for consistency and fix typings * support evaluation options for PPO baseline code, remove old videos, refactor force_use_gpu_sim to sim_backend, bug fix where seed was null in saved trajectory jsons * improved docs w.r.t installation * versioin bump * Fix RotateCubeLevel1 Rotation Init Error (#256) * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * update rotate_cube v1 * Peg insertion side batched dense reward, simplify utilities code and refactoring (#259) * init * clean up utility functions, refactoring some out into new files * merge conflicts, refactoring * simplify imports * refactorings * Update sapien_utils.py * modify tutorial tasks to always include the common utilities module * Update sapien_utils.py * fix circular import errors * bug fix with index_dict_array, affecting record episode wrapper * more flexible pose setting for merged actors/articulation views on CPU (#260) * support setting pose on a view of actors in CPU sim, add device property to pose struct * flexible pose setting for links as well * Clamp and normalize reward of RotateCubeEnv (#261) * support controllers configuring balance_passive_force (#263) * version bump * typo fix in template robot file * Fix memory leaks caused by bad cache for visual obs modes, fix printing of actors/articulations in pdb modes, clear sim state after reconfigures (#264) * fix cpu sim mem leak with visual obs and constant reconfiguring * more fixes * remove old code * Update mem_leak_test.py * version bump * Fix camera not accepting intrinsic argument, small refactor of add_camera API, and add peg insertion to test matrices (#267) * work * add peg insertion side to test matrices * Update test_gpu_envs.py * fix asset download for ReplicaCAD / hf repo datasets (to be moved in the future) * Add segmentation data w/ docs + demos, fix replay trajectory test, change OrderedDict to dict in some places (#270) * work * fix replay trajectory tests to use new directory * Update test_replay_trajectory.py * Update test_replay_trajectory.py * deprecate env.get_actors and env.get_articulations. * demo code * fix demo code * Update observation.md * Update sapien_env.py * Update sapien_env.py * version bump * fix fast_kinematics version as latest one may be buggy as its in beta * fix bug where extrinsic matrix was incorrectly cached when there is a mount * Update render_camera.py * add appropriate tests * fix tests * Fix: use physx.is_gpu_enabled() instead of self.num_envs > 1 (#272) * MJCF Loader for SAPIEN/ManiSkill and example CartPole task from DM Control, New QoL features for joints (#273) * move controller config setup after articulation is loaded for robots to give user access to active joints easily * change fix_root_link to be a agent property * work * work * fixes * Update mjcf_loader.py * work * code cleanup, use joint pos correctly * work * fixes * work * a1 loading test * Update _mjcf_loader.py * support loading other objects in xml * cartpole task implemented from xml * cart pole task, fetch qpos by joint * work * docs * fix controllers, use disable gravity to balance passive forces on CPU and GPU. * Update sapien_env.py * more runtime errors * tuned ppo for cartpole * some minor fixes * cart pole video demo * docs * H1 robot (#275) * work * cleaner code * init * fix urdf mesh paths * provide a typical "standing" keyframe * fix a few files * some useful scripts * work * new envs, simplified contact version of h1 robot * work * Tutorial on how to add robots (#276) * work * cleaner code * work * work * Update custom_robots.md * Update custom_robots.md * Update controllers.md * Code refactor for better naming (#277) * default_sim_cfg -> default_sim_config to follow semantics of other code * _sensor_configs->_default_sensor_configs, _human_render_camera_configs->_default_human_render_camera_configs * remove _parsed * remove OrderedDict in favor of dict and removing old code * fixing up anymal and refactoring * add friction to robot feet to let it stand easily, procedurally generated checkerboard * Update ground.py * tuning * work * Update anymal_c.py * refactor * update benchmarking code * work * tune joint limits for easy rl * use simplified collision mesh * work * revert * revert * revert * revert * revert * work * Delete floor_tiles_06_2k.glb --------- Co-authored-by: Arnav G <73305591+arnavg115@users.noreply.github.com> Co-authored-by: Arnav G Co-authored-by: arth-shukla <44527268+arth-shukla@users.noreply.github.com> Co-authored-by: itsygao Co-authored-by: ygaoproj <102192613+ygaoproj@users.noreply.github.com> Co-authored-by: Yuzhe Qin Co-authored-by: smtrived <104041864+smtrived@users.noreply.github.com> Co-authored-by: Fanbo Xiang Co-authored-by: Chen Bao Co-authored-by: Chen Bao --- CONTRIBUTING.md | 2 +- mani_skill/agents/base_agent.py | 5 + mani_skill/agents/robots/anymal/anymal_c.py | 106 +- .../assets/robots/anymal-c/urdf/anymal.urdf | 322 +--- .../robots/anymal-c/urdf/anymal_old.urdf | 1602 +++++++++++++++++ .../robots/anymal-c/urdf/anymal_original.urdf | 1602 +++++++++++++++++ mani_skill/envs/tasks/__init__.py | 3 +- mani_skill/envs/tasks/humanoid/README.md | 3 + mani_skill/envs/tasks/quadruped/README.md | 3 + mani_skill/envs/tasks/quadruped/__init__.py | 1 + .../envs/tasks/quadruped/quadruped_reach.py | 138 ++ mani_skill/envs/tasks/quadruped_run.py | 120 -- mani_skill/envs/tasks/quadruped_stand.py | 99 - mani_skill/examples/benchmarking/README.md | 10 +- mani_skill/examples/demo_random_action.py | 7 +- .../motionplanning/grasp_pose/utils.py | 258 --- .../building/assets/floor_tiles_06_2k.glb | Bin 17487172 -> 0 bytes .../assets/floor_tiles_06_diff_2k_aligned.png | Bin 0 -> 2990251 bytes mani_skill/utils/building/ground.py | 103 +- mani_skill/utils/download_asset.py | 4 + mani_skill/utils/structs/link.py | 6 + 21 files changed, 3551 insertions(+), 843 deletions(-) create mode 100644 mani_skill/assets/robots/anymal-c/urdf/anymal_old.urdf create mode 100644 mani_skill/assets/robots/anymal-c/urdf/anymal_original.urdf create mode 100644 mani_skill/envs/tasks/humanoid/README.md create mode 100644 mani_skill/envs/tasks/quadruped/README.md create mode 100644 mani_skill/envs/tasks/quadruped/__init__.py create mode 100644 mani_skill/envs/tasks/quadruped/quadruped_reach.py delete mode 100644 mani_skill/envs/tasks/quadruped_run.py delete mode 100644 mani_skill/envs/tasks/quadruped_stand.py delete mode 100644 mani_skill/examples/motionplanning/grasp_pose/utils.py delete mode 100644 mani_skill/utils/building/assets/floor_tiles_06_2k.glb create mode 100644 mani_skill/utils/building/assets/floor_tiles_06_diff_2k_aligned.png diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 1c263da53..d32d114b9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -68,4 +68,4 @@ python -m twine upload dist/* ManiSkill is built to support building your own custom tasks easily. The documentation on how to use the ManiSkill API to do so is here: https://maniskill.readthedocs.io/en/latest/user_guide/tutorials/custom_tasks.html -We encourage users to either create their own repositories with their own tasks for others to use, or to submit to this ManiSkill repo to be part of the official, *vetted*, task list. For tasks in this repo, we do a number of checks to ensure they are of high quality and are well documented. For detailed information on how to add new tasks, see https://maniskill.readthedocs.io/en/latest/contributing/task.html \ No newline at end of file +We encourage users to either create their own repositories with their own tasks for others to use, or to submit to this ManiSkill repo to be part of the official, *vetted*, task list. For tasks in this repo, we do a number of checks to ensure they are of high quality and are well documented. For detailed information on how to add new tasks, see https://maniskill.readthedocs.io/en/latest/contributing/task.html diff --git a/mani_skill/agents/base_agent.py b/mani_skill/agents/base_agent.py index 8b89d32d2..3c44701b0 100644 --- a/mani_skill/agents/base_agent.py +++ b/mani_skill/agents/base_agent.py @@ -60,6 +60,10 @@ class BaseAgent: """Whether to fix the root link of the robot""" load_multiple_collisions: bool = False """Whether the referenced collision meshes of a robot definition should be loaded as multiple convex collisions""" + disable_self_collisions: bool = False + """Whether to disable self collisions. This is generally not recommended as you should be defining a SRDF file to exclude specific collisions. + However for some robots/tasks it may be easier to disable all self collisions between links in the robot to increase simulation speed + """ keyframes: Dict[str, Keyframe] = dict() """a dict of predefined keyframes similar to what Mujoco does that you can use to reset the agent to that may be of interest""" @@ -123,6 +127,7 @@ def _load_articulation(self): loader.name = f"{self.uid}-agent-{self._agent_idx}" loader.fix_root_link = self.fix_root_link loader.load_multiple_collisions_from_file = self.load_multiple_collisions + loader.disable_self_collisions = self.disable_self_collisions if self.urdf_config is not None: urdf_config = sapien_utils.parse_urdf_config(self.urdf_config) diff --git a/mani_skill/agents/robots/anymal/anymal_c.py b/mani_skill/agents/robots/anymal/anymal_c.py index 27c2b3ff2..9701c9c53 100644 --- a/mani_skill/agents/robots/anymal/anymal_c.py +++ b/mani_skill/agents/robots/anymal/anymal_c.py @@ -1,7 +1,10 @@ +# TODO (stao): Anymal may not be modelled correctly or efficiently at the moment +import numpy as np +import sapien import torch -from mani_skill import PACKAGE_ASSET_DIR, format_path -from mani_skill.agents.base_agent import BaseAgent +from mani_skill import PACKAGE_ASSET_DIR +from mani_skill.agents.base_agent import BaseAgent, Keyframe from mani_skill.agents.controllers import * from mani_skill.agents.registration import register_agent from mani_skill.utils import sapien_utils @@ -11,33 +14,53 @@ @register_agent() class ANYmalC(BaseAgent): uid = "anymal-c" - urdf_path = f"{PACKAGE_ASSET_DIR}/robots/anymal-c/urdf/anymal.urdf" - urdf_config = dict() + urdf_path = f"{PACKAGE_ASSET_DIR}/robots/anymal-c/urdf/anymal_old.urdf" + urdf_config = dict( + _materials=dict( + foot=dict(static_friction=2.0, dynamic_friction=2.0, restitution=0.0) + ), + link=dict( + LF_FOOT=dict(material="foot", patch_radius=0.1, min_patch_radius=0.1), + LH_FOOT=dict(material="foot", patch_radius=0.1, min_patch_radius=0.1), + RF_FOOT=dict(material="foot", patch_radius=0.1, min_patch_radius=0.1), + RH_FOOT=dict(material="foot", patch_radius=0.1, min_patch_radius=0.1), + ), + ) fix_root_link = False + disable_self_collisions = True + + keyframes = dict( + standing=Keyframe( + pose=sapien.Pose(p=[0, 0, 0.545]), + qpos=np.array( + [0.03, -0.03, 0.03, -0.03, 0.4, 0.4, -0.4, -0.4, -0.8, -0.8, 0.8, 0.8] + ), + ) + ) + + joint_names = [ + "LF_HAA", + "RF_HAA", + "LH_HAA", + "RH_HAA", + "LF_HFE", + "RF_HFE", + "LH_HFE", + "RH_HFE", + "LF_KFE", + "RF_KFE", + "LH_KFE", + "RH_KFE", + ] def __init__(self, *args, **kwargs): - self.joint_names = [ - "LF_HAA", - "RF_HAA", - "LH_HAA", - "RH_HAA", - "LF_HFE", - "RF_HFE", - "LH_HFE", - "RH_HFE", - "LF_KFE", - "RF_KFE", - "LH_KFE", - "RH_KFE", - ] super().__init__(*args, **kwargs) @property def _controller_configs(self): - self.arm_stiffness = 85.0 + self.arm_stiffness = 80.0 self.arm_damping = 2.0 self.arm_force_limit = 100 - # delta action scale for Omni Isaac Gym Envs is self.dt * self.action_scale = 1/60 * 13.5. NOTE that their self.dt value is not the same as the actual DT used in sim...., they use default of 1/100 pd_joint_delta_pos = PDJointPosControllerConfig( self.joint_names, @@ -59,37 +82,22 @@ def _controller_configs(self): normalize_action=False, use_delta=False, ) + # TODO (stao): For quadrupeds perhaps we disable gravit for all links except the root? controller_configs = dict( - pd_joint_delta_pos=pd_joint_delta_pos, pd_joint_pos=pd_joint_pos + pd_joint_delta_pos=dict( + body=pd_joint_delta_pos, balance_passive_force=False + ), + pd_joint_pos=dict(body=pd_joint_pos, balance_passive_force=False), ) return controller_configs def _after_init(self): - pass - - def _load_articulation(self): - """ - Load the robot articulation - """ - loader = self.scene.create_urdf_loader() - loader.name = self.uid - if self._agent_idx is not None: - loader.name = f"{self.uid}-agent-{self._agent_idx}" - loader.fix_root_link = self.fix_root_link - - urdf_path = format_path(str(self.urdf_path)) - - urdf_config = sapien_utils.parse_urdf_config(self.urdf_config) - sapien_utils.check_urdf_config(urdf_config) - - # TODO(jigu): support loading multiple convex collision shapes - sapien_utils.apply_urdf_config(loader, urdf_config) - loader.disable_self_collisions = True - self.robot: Articulation = loader.load(urdf_path) - assert self.robot is not None, f"Fail to load URDF from {urdf_path}" + # disable gravity / compensate gravity automatically in all links but the root one + for link in self.robot.links[1:]: + link.disable_gravity = True - def is_standing(self): - """This quadruped is considered standing if it is face up and body is at least 0.5m off the ground""" + def is_standing(self, ground_height=0): + """This quadruped is considered standing if it is face up and body is at least 0.35m off the ground""" target_q = torch.tensor([1, 0, 0, 0], device=self.device) inner_prod = (self.robot.pose.q * target_q).sum(axis=1) # angle_diff = 1 - (inner_prod ** 2) # computes a distance from 0 to 1 between 2 quaternions @@ -98,7 +106,9 @@ def is_standing(self): ) # computes an angle between 2 quaternions # about 20 degrees aligned = angle_diff < 0.349 - high_enough = self.robot.pose.p[:, 2] > 0.5 - return torch.logical_and(aligned, high_enough) + high_enough = self.robot.pose.p[:, 2] > 0.35 + ground_height + return aligned & high_enough - sensor_configs = [] + def is_fallen(self, ground_height=0): + """This quadruped is considered fallen if its body is 0.175m off the ground""" + return self.robot.pose.p[:, 2] < 0.175 + ground_height diff --git a/mani_skill/assets/robots/anymal-c/urdf/anymal.urdf b/mani_skill/assets/robots/anymal-c/urdf/anymal.urdf index f85aa13b3..92c3f9a8a 100644 --- a/mani_skill/assets/robots/anymal-c/urdf/anymal.urdf +++ b/mani_skill/assets/robots/anymal-c/urdf/anymal.urdf @@ -13,51 +13,11 @@ - - - - - - - - - - - - - - - - - - - - - - - - - + - + - - - - - - - - - - - - - - - - @@ -158,24 +118,6 @@ - - - - - - - - - - - - - - - - - - @@ -279,24 +221,6 @@ - - - - - - - - - - - - - - - - - - @@ -429,12 +353,6 @@ - - - - - - @@ -568,12 +486,6 @@ - - - - - - @@ -626,7 +538,7 @@ - + @@ -671,12 +583,6 @@ - - - - - - @@ -689,7 +595,7 @@ - + @@ -714,19 +620,6 @@ - - - - - - - - - - - - - @@ -750,7 +643,7 @@ - + @@ -765,7 +658,7 @@ - + @@ -775,6 +668,13 @@ + + + + + + + @@ -790,19 +690,6 @@ - - - - - - - - - - - - - @@ -811,7 +698,7 @@ - + @@ -824,13 +711,6 @@ - - - - - - - @@ -870,7 +750,7 @@ - + @@ -915,12 +795,6 @@ - - - - - - @@ -933,7 +807,7 @@ - + @@ -958,19 +832,6 @@ - - - - - - - - - - - - - @@ -994,7 +855,7 @@ - + @@ -1009,7 +870,7 @@ - + @@ -1019,6 +880,13 @@ + + + + + + + @@ -1034,19 +902,6 @@ - - - - - - - - - - - - - @@ -1055,7 +910,7 @@ - + @@ -1068,13 +923,6 @@ - - - - - - - @@ -1114,7 +962,7 @@ - + @@ -1159,12 +1007,6 @@ - - - - - - @@ -1177,7 +1019,7 @@ - + @@ -1202,19 +1044,6 @@ - - - - - - - - - - - - - @@ -1238,7 +1067,7 @@ - + @@ -1253,7 +1082,7 @@ - + @@ -1263,6 +1092,13 @@ + + + + + + + @@ -1279,18 +1115,6 @@ - - - - - - - - - - - - @@ -1299,7 +1123,7 @@ - + @@ -1312,13 +1136,6 @@ - - - - - - - @@ -1358,7 +1175,7 @@ - + @@ -1403,12 +1220,6 @@ - - - - - - @@ -1421,7 +1232,7 @@ - + @@ -1446,19 +1257,6 @@ - - - - - - - - - - - - - @@ -1482,7 +1280,7 @@ - + @@ -1497,7 +1295,7 @@ - + @@ -1507,6 +1305,13 @@ + + + + + + + @@ -1522,19 +1327,6 @@ - - - - - - - - - - - - - @@ -1543,7 +1335,7 @@ - + @@ -1556,13 +1348,6 @@ - - - - - - - @@ -1593,10 +1378,7 @@ - - - + - - + \ No newline at end of file diff --git a/mani_skill/assets/robots/anymal-c/urdf/anymal_old.urdf b/mani_skill/assets/robots/anymal-c/urdf/anymal_old.urdf new file mode 100644 index 000000000..978f2e1d1 --- /dev/null +++ b/mani_skill/assets/robots/anymal-c/urdf/anymal_old.urdf @@ -0,0 +1,1602 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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a/mani_skill/assets/robots/anymal-c/urdf/anymal_original.urdf b/mani_skill/assets/robots/anymal-c/urdf/anymal_original.urdf new file mode 100644 index 000000000..f85aa13b3 --- /dev/null +++ b/mani_skill/assets/robots/anymal-c/urdf/anymal_original.urdf @@ -0,0 +1,1602 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/mani_skill/envs/tasks/__init__.py b/mani_skill/envs/tasks/__init__.py index ff4dc0e88..917ca3389 100644 --- a/mani_skill/envs/tasks/__init__.py +++ b/mani_skill/envs/tasks/__init__.py @@ -4,7 +4,6 @@ from .fmb import FMBAssembly1Env from .humanoid import * from .open_cabinet_drawer import OpenCabinetDoorEnv, OpenCabinetDrawerEnv -from .quadruped_run import QuadrupedRunEnv -from .quadruped_stand import QuadrupedStandEnv +from .quadruped import * from .rotate_cube import RotateCubeEnv from .tabletop import * diff --git a/mani_skill/envs/tasks/humanoid/README.md b/mani_skill/envs/tasks/humanoid/README.md new file mode 100644 index 000000000..f9475c005 --- /dev/null +++ b/mani_skill/envs/tasks/humanoid/README.md @@ -0,0 +1,3 @@ +# Humanoid Tasks + +These are WIP without tuned sim configs and dense rewards yet \ No newline at end of file diff --git a/mani_skill/envs/tasks/quadruped/README.md b/mani_skill/envs/tasks/quadruped/README.md new file mode 100644 index 000000000..eead19cbf --- /dev/null +++ b/mani_skill/envs/tasks/quadruped/README.md @@ -0,0 +1,3 @@ +# Quadruped Tasks + +These are WIP and don't have tuned sim configs / dense rewards yet \ No newline at end of file diff --git a/mani_skill/envs/tasks/quadruped/__init__.py b/mani_skill/envs/tasks/quadruped/__init__.py new file mode 100644 index 000000000..726b09ff3 --- /dev/null +++ b/mani_skill/envs/tasks/quadruped/__init__.py @@ -0,0 +1 @@ +from .quadruped_reach import QuadrupedReachEnv diff --git a/mani_skill/envs/tasks/quadruped/quadruped_reach.py b/mani_skill/envs/tasks/quadruped/quadruped_reach.py new file mode 100644 index 000000000..f1e37ab50 --- /dev/null +++ b/mani_skill/envs/tasks/quadruped/quadruped_reach.py @@ -0,0 +1,138 @@ +from typing import Any, Dict + +import numpy as np +import sapien +import torch +from transforms3d.euler import euler2quat + +from mani_skill.agents.robots.anymal.anymal_c import ANYmalC +from mani_skill.envs.sapien_env import BaseEnv +from mani_skill.sensors.camera import CameraConfig +from mani_skill.utils import sapien_utils +from mani_skill.utils.building import actors +from mani_skill.utils.building.ground import build_ground +from mani_skill.utils.registration import register_env +from mani_skill.utils.structs.pose import Pose +from mani_skill.utils.structs.types import GPUMemoryConfig, SceneConfig, SimConfig + + +class QuadrupedReachEnv(BaseEnv): + SUPPORTED_ROBOTS = ["anymal-c"] + agent: ANYmalC + + def __init__(self, *args, robot_uids="anymal-c", **kwargs): + super().__init__(*args, robot_uids=robot_uids, **kwargs) + + @property + def _default_sim_config(self): + return SimConfig( + gpu_memory_cfg=GPUMemoryConfig(max_rigid_contact_count=2**20), + scene_cfg=SceneConfig( + solver_position_iterations=4, solver_velocity_iterations=0 + ), + ) + + @property + def _default_sensor_configs(self): + pose = sapien_utils.look_at(eye=[0.5, 0, 0.1], target=[1.0, 0, 0.0]) + return [ + CameraConfig( + "base_camera", + pose=pose, + width=128, + height=128, + fov=np.pi / 2, + near=0.01, + far=100, + mount=self.agent.robot.links[0], + ) + ] + + @property + def _default_human_render_camera_configs(self): + pose = sapien_utils.look_at([-2.5, 0.5, 2], [0.0, 0.0, 0]) + return [ + CameraConfig( + "render_camera", + pose=pose, + width=512, + height=512, + fov=1, + near=0.01, + far=100, + mount=self.agent.robot.links[0], + ) + ] + + def _load_scene(self, options: dict): + self.ground = build_ground(self._scene) + self.goal = actors.build_sphere( + self._scene, + radius=0.2, + color=[0, 1, 0, 1], + name="goal", + add_collision=False, + body_type="kinematic", + ) + + def _initialize_episode(self, env_idx: torch.Tensor, options: dict): + with torch.device(self.device): + b = len(env_idx) + keyframe = self.agent.keyframes["standing"] + self.agent.robot.set_pose(keyframe.pose) + self.agent.robot.set_qpos(keyframe.qpos) + # sample random goal + xyz = torch.zeros((b, 3)) + noise_scale = 1 + xyz[:, 0] = torch.rand(size=(b,)) * noise_scale - noise_scale / 2 + 2.5 + noise_scale = 4 + xyz[:, 1] = torch.rand(size=(b,)) * noise_scale - noise_scale / 2 + self.goal.set_pose(Pose.create_from_pq(xyz)) + + def evaluate(self): + is_fallen = self.agent.is_fallen() + robot_to_goal_dist = torch.linalg.norm( + self.goal.pose.p[:, :2] - self.agent.robot.pose.p[:, :2], axis=1 + ) + reached_goal = robot_to_goal_dist < 0.35 + return { + "success": reached_goal & ~is_fallen, + "fail": is_fallen, + "robot_to_goal_dist": robot_to_goal_dist, + "reached_goal": reached_goal, + "is_fallen": is_fallen, + } + + def _get_obs_extra(self, info: Dict): + obs = dict() + if self.obs_mode in ["state", "state_dict"]: + obs.update( + goal_pos=self.goal.pose.p[:, :2], + robot_to_goal=self.goal.pose.p[:, :2] - self.agent.robot.pose.p[:, :2], + ) + return obs + + def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict): + robot_to_goal_dist = info["robot_to_goal_dist"] + reaching_reward = 1 - torch.tanh(1 * robot_to_goal_dist) + + # various penalties: + lin_vel_z_l2 = torch.square(self.agent.robot.root_linear_velocity[:, 2]) + ang_vel_xy_l2 = ( + torch.square(self.agent.robot.root_angular_velocity[:, :2]) + ).sum(axis=1) + penalties = lin_vel_z_l2 * -0.15 + ang_vel_xy_l2 * -0.05 + reward = reaching_reward + penalties + return reward + + def compute_normalized_dense_reward( + self, obs: Any, action: torch.Tensor, info: Dict + ): + max_reward = 1.0 + return self.compute_dense_reward(obs=obs, action=action, info=info) / max_reward + + +# @register_env("AnymalC-Reach-v1", max_episode_steps=200) +class AnymalCReachEnv(QuadrupedReachEnv): + def __init__(self, *args, robot_uids="anymal-c", **kwargs): + super().__init__(*args, robot_uids=robot_uids, **kwargs) diff --git a/mani_skill/envs/tasks/quadruped_run.py b/mani_skill/envs/tasks/quadruped_run.py deleted file mode 100644 index 9db0cce32..000000000 --- a/mani_skill/envs/tasks/quadruped_run.py +++ /dev/null @@ -1,120 +0,0 @@ -from typing import Any, Dict - -import numpy as np -import sapien -import torch -from transforms3d.euler import euler2quat - -from mani_skill.agents.robots.anymal.anymal_c import ANYmalC -from mani_skill.agents.robots.fetch.fetch import Fetch -from mani_skill.agents.robots.panda.panda import Panda -from mani_skill.envs.sapien_env import BaseEnv -from mani_skill.sensors.camera import CameraConfig -from mani_skill.utils import sapien_utils -from mani_skill.utils.building import actors -from mani_skill.utils.building.ground import build_ground, build_meter_ground -from mani_skill.utils.registration import register_env -from mani_skill.utils.structs.pose import Pose -from mani_skill.utils.structs.types import GPUMemoryConfig, SceneConfig, SimConfig - - -# @register_env("QuadrupedRun-v1", max_episode_steps=200) -class QuadrupedRunEnv(BaseEnv): - - SUPPORTED_ROBOTS = ["anymal-c"] - agent: ANYmalC - - def __init__(self, *args, robot_uids="anymal-c", **kwargs): - super().__init__(*args, robot_uids=robot_uids, **kwargs) - - """ - NOTE that Isaac Anymal has these settings - why is their patch/contact count so low compared to ours? - gpu_max_rigid_contact_count: 524288 - gpu_max_rigid_patch_count: 163840 - gpu_found_lost_pairs_capacity: 4194304 - gpu_found_lost_aggregate_pairs_capacity: 33554432 - gpu_total_aggregate_pairs_capacity: 4194304 - """ - - @property - def _default_sim_config(self): - return SimConfig( - sim_freq=100, - control_freq=50, - gpu_memory_cfg=GPUMemoryConfig( - heap_capacity=2**27, - temp_buffer_capacity=2**25, - found_lost_pairs_capacity=2**22, - found_lost_aggregate_pairs_capacity=2**25, - total_aggregate_pairs_capacity=2**22, - max_rigid_patch_count=2**18, - max_rigid_contact_count=2**20, - ), - scene_cfg=SceneConfig( - solver_position_iterations=4, - bounce_threshold=0.2, - ), - ) - - @property - def _default_sensor_configs(self): - pose = sapien_utils.look_at([1.5, 1.5, 1], [0.0, 0.0, 0]) - return [ - CameraConfig( - "base_camera", - pose.p, - pose.q, - 128, - 128, - np.pi / 2, - 0.01, - 100, - # link=self.agent.robot.links[0], - ) - ] - - @property - def _default_human_render_camera_configs(self): - pose = sapien_utils.look_at([2.5, 2.5, 1], [0.0, 0.0, 0]) - return CameraConfig( - "render_camera", - pose.p, - pose.q, - 512, - 512, - 1, - 0.01, - 100, - # link=self.agent.robot.links[0], - ) - - def _load_scene(self, options: dict): - self.ground = build_meter_ground(self._scene, floor_width=20) - self.height = 0.63 - - def _initialize_episode(self, env_idx: torch.Tensor, options: dict): - with torch.device(self.device): - self.agent.robot.set_pose(Pose.create_from_pq(p=[0, 0, self.height])) - self.agent.reset(init_qpos=torch.zeros(self.agent.robot.max_dof)) - - def evaluate(self): - return {"success": torch.zeros(self.num_envs, dtype=bool, device=self.device)} - - def _get_obs_extra(self, info: Dict): - return dict() - - def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict): - xvel = self.agent.robot.root_linear_velocity[:, 0] - # cts = self._scene.get_contacts() - # print(len(cts)) - # for ct in cts: - # # if ct.bodies[1].entity.name == "base" or ct.bodies[0].entity.name == "base": - # print(ct) - return xvel / 10 - - def compute_normalized_dense_reward( - self, obs: Any, action: torch.Tensor, info: Dict - ): - max_reward = 1.0 - return self.compute_dense_reward(obs=obs, action=action, info=info) / max_reward diff --git a/mani_skill/envs/tasks/quadruped_stand.py b/mani_skill/envs/tasks/quadruped_stand.py deleted file mode 100644 index 5e1d09aa6..000000000 --- a/mani_skill/envs/tasks/quadruped_stand.py +++ /dev/null @@ -1,99 +0,0 @@ -from typing import Any, Dict - -import numpy as np -import sapien -import torch -from transforms3d.euler import euler2quat - -from mani_skill.agents.robots.anymal.anymal_c import ANYmalC -from mani_skill.agents.robots.fetch.fetch import Fetch -from mani_skill.agents.robots.panda.panda import Panda -from mani_skill.envs.sapien_env import BaseEnv -from mani_skill.sensors.camera import CameraConfig -from mani_skill.utils import sapien_utils -from mani_skill.utils.building import actors -from mani_skill.utils.building.ground import build_ground, build_meter_ground -from mani_skill.utils.registration import register_env -from mani_skill.utils.structs.pose import Pose - - -# @register_env("QuadrupedStand-v1", max_episode_steps=200) -class QuadrupedStandEnv(BaseEnv): - - SUPPORTED_ROBOTS = ["anymal-c"] - agent: ANYmalC - - def __init__(self, *args, robot_uids="anymal-c", **kwargs): - super().__init__(*args, robot_uids=robot_uids, **kwargs) - - @property - def _default_sensor_configs(self): - pose = sapien_utils.look_at(eye=[0.3, 0, 0.6], target=[-0.1, 0, 0.1]) - return [ - CameraConfig( - "base_camera", - pose.p, - pose.q, - 128, - 128, - np.pi / 2, - 0.01, - 100, - mount=self.agent.robot.links[0], - ) - ] - - @property - def _default_human_render_camera_configs(self): - pose = sapien_utils.look_at([2.5, 2.5, 1], [0.0, 0.0, 0]) - return CameraConfig( - "render_camera", - pose.p, - pose.q, - 512, - 512, - 1, - 0.01, - 100, - mount=self.agent.robot.links[0], - ) - - def _load_scene(self, options: dict): - # for i in range(10): - # ground = build_ground(self._scene, return_builder=True) - # ground.initial_pose = sapien.Pose(p=[i * 40, 0, 0]) - # ground.build_static(name="ground") - # self.ground = self._scene.create_actor_builder() - self.ground = build_meter_ground(self._scene, floor_width=1000) - self.cube = actors.build_cube( - self._scene, 0.05, color=[1, 0, 0, 1], name="cube" - ) - # TODO (stao): why is this collision mesh so wacky? - # mesh = self.agent.robot.get_collision_mesh(first_only=True) - # self.height = -mesh[0].bounding_box.bounds[0, 2] - self.height = 1.626 - - def _initialize_episode(self, env_idx: torch.Tensor, options: dict): - with torch.device(self.device): - self.agent.robot.set_pose(Pose.create_from_pq(p=[0, 0, self.height])) - self.agent.reset(init_qpos=torch.zeros(self.agent.robot.max_dof)) - self.cube.set_pose(Pose.create_from_pq(p=[0, 0, 0.05])) - - def evaluate(self): - forces = self.agent.robot.get_net_contact_forces(["RH_KFE", "LH_KFE"]).norm( - dim=(1, 2) - ) - return {"success": self.agent.is_standing()} - - def _get_obs_extra(self, info: Dict): - return dict(robot_pose=self.agent.robot.pose.raw_pose) - - def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict): - reward = info["success"] - return reward - - def compute_normalized_dense_reward( - self, obs: Any, action: torch.Tensor, info: Dict - ): - max_reward = 1.0 - return self.compute_dense_reward(obs=obs, action=action, info=info) / max_reward diff --git a/mani_skill/examples/benchmarking/README.md b/mani_skill/examples/benchmarking/README.md index 72832d960..c9dacf320 100644 --- a/mani_skill/examples/benchmarking/README.md +++ b/mani_skill/examples/benchmarking/README.md @@ -8,16 +8,16 @@ Code here is used to benchmark the performance of various simulators/benchmarks To benchmark ManiSkill + SAPIEN, after following the setup instructions on this repository's README.md, run ``` -python benchmark_maniskill.py -e "PickCube-v1" --num-envs=1024 --obs-mode=state # test just state simulation -python benchmark_maniskill.py -e "PickCube-v1" --num-envs=128 --obs-mode=rgbd # test state sim + parallel rendering one 128x128 RGBD cameras per environment -python benchmark_maniskill.py -e "PickCube-v1" --num-envs=128 --save-video # save a video showing all 128 visual observations +python gpu_sim.py -e "PickCube-v1" --num-envs=1024 --obs-mode=state # test just state simulation +python gpu_sim.py -e "PickCube-v1" --num-envs=128 --obs-mode=rgbd # test state sim + parallel rendering one 128x128 RGBD cameras per environment +python gpu_sim.py -e "PickCube-v1" --num-envs=128 --save-video # save a video showing all 128 visual observations ``` To get the reported results, we run two commands on a machine with a RTX 4090: ``` -python benchmark_maniskill.py -e "PickCube-v1" --num-envs=4096 --obs-mode=state --control-freq=50 -python benchmark_maniskill.py -e "PickCube-v1" --num-envs=1536 --obs-mode=rgbd --control-freq=50 +python gpu_sim.py -e "PickCube-v1" --num-envs=4096 --obs-mode=state --control-freq=50 +python gpu_sim.py -e "PickCube-v1" --num-envs=1536 --obs-mode=rgbd --control-freq=50 # note we use --control-freq=50 as this is the control frequency isaac sim based repos tend to use ``` diff --git a/mani_skill/examples/demo_random_action.py b/mani_skill/examples/demo_random_action.py index 505dde826..fe65acf68 100644 --- a/mani_skill/examples/demo_random_action.py +++ b/mani_skill/examples/demo_random_action.py @@ -14,9 +14,10 @@ def parse_args(args=None): parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'") parser.add_argument("--reward-mode", type=str) parser.add_argument("-c", "--control-mode", type=str) - parser.add_argument("--render-mode", type=str, default="rgb_array", help="Can be 'human' to open a viewer, or rgb_array / sensors which change the cameras saved videos use") + parser.add_argument("--render-mode", type=str) parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer") - parser.add_argument("--record-dir", type=str, help="Where to save recorded videos. If none, no videos are saved") + parser.add_argument("--record-dir", type=str) + parser.add_argument("-p", "--pause", action="store_true", help="If using human render mode, auto pauses the simulation upon loading") parser.add_argument("--quiet", action="store_true", help="Disable verbose output.") parser.add_argument( "-s", @@ -68,6 +69,8 @@ def main(args): obs, _ = env.reset(seed=args.seed) env.action_space.seed(args.seed) if args.render_mode is not None: + viewer = env.render() + viewer.paused = args.pause env.render() while True: diff --git a/mani_skill/examples/motionplanning/grasp_pose/utils.py b/mani_skill/examples/motionplanning/grasp_pose/utils.py deleted file mode 100644 index c0febdbef..000000000 --- a/mani_skill/examples/motionplanning/grasp_pose/utils.py +++ /dev/null @@ -1,258 +0,0 @@ -import numpy as np -import open3d as o3d -import tqdm -import transforms3d -from pymp.robot import RobotWrapper -from pymp.utils import toSE3 -from scipy.spatial.distance import cdist -from scipy.spatial.transform import Rotation - - -def norm_vec(x, eps=1e-6): - norm = np.linalg.norm(x, axis=-1, keepdims=True) - return np.where(norm < eps, 0, x / np.maximum(norm, eps)) - - -def rotate_transform(R): - out = np.zeros(R.shape[:-2] + (4, 4)) - out[..., :3, :3] = R - out[..., 3, 3] = 1 - return out - - -def compute_antipodal_contact_points(points, normals, max_width, score_thresh): - dist = cdist(points, points) - - # Distance between two contact points should be larger than gripper width. - mask1 = dist <= max_width - - # [n, n, 3], direction between two surface points - direction = norm_vec(points[None, :] - points[:, None]) - # [n, n] - cos_angle = np.squeeze(direction @ normals[..., None], -1) - - # Heuristic from S4G - score = np.abs(cos_angle * cos_angle.T) - mask2 = score >= score_thresh - # print(score[0:5, 0:5]) - - row, col = np.nonzero(np.triu(np.logical_and(mask1, mask2), k=1)) - return row, col, score[row, col] - - -def initialize_grasp_poses(points, row, col): - # Assume the grasp frame is approaching (x), closing (y), ortho (z). - # The origin is the center of two contact points. - # Please convert to the gripper you use. - - # The closing vector is segment between two contact points - displacement = points[col] - points[row] - closing_vec = norm_vec(displacement) # [m, 3] - # Approaching and orthogonal vectors should be searched later. - U, _, _ = np.linalg.svd(closing_vec[..., None]) # [m, 3, 3] - approaching_vec = U[..., 1] # [m, 3] - assert np.all(np.einsum("nd,nd->n", approaching_vec, closing_vec) <= 1e-6) - center = (points[col] + points[row]) * 0.5 - - grasp_frames = np.tile(np.eye(4), [len(row), 1, 1]) - grasp_frames[:, 0:3, 0] = approaching_vec - grasp_frames[:, 0:3, 1] = closing_vec - grasp_frames[:, 0:3, 2] = np.cross(approaching_vec, closing_vec) - grasp_frames[:, 0:3, 3] = center - return grasp_frames, np.linalg.norm(displacement, axis=-1) * 0.5 - - -def augment_grasp_poses(grasp_poses, angles): - Rs = Rotation.from_euler("y", angles).as_matrix() # [A, 3, 3] - Ts = rotate_transform(Rs) # [A, 4, 4] - out = np.einsum("nij,mjk->nmik", grasp_poses, Ts) - return out - - -def compute_grasp_poses( - mesh_path, - n_pts, - gripper_urdf, - gripper_srdf, - tcp_link_name, - mesh_scale=1.0, - gripper_width=0.08, - open_ratio=0.95, - score_thresh=0.97, - n_angles=36, - octree_resolution=0.001, - open_widths=None, - add_ground=False, - seed=0, - vis=False, -): - # Load shape - mesh = o3d.io.read_triangle_mesh(str(mesh_path)) - mesh.compute_vertex_normals() - mesh.scale(mesh_scale, center=(0, 0, 0)) - min_bound = mesh.get_min_bound() - # Open3d interpolates normals for sampled points. - pcd = mesh.sample_points_uniformly(number_of_points=n_pts) - points, normals = np.array(pcd.points), np.array(pcd.normals) - - # 1. Generate contact point pairs - row, col, score = compute_antipodal_contact_points( - points, normals, gripper_width * open_ratio, score_thresh - ) - print("#contact points", len(row)) - - # 2. Build initial grasp poses - grasp_poses, closing_dists = initialize_grasp_poses(points, row, col) - - # 3. Search feasible grasp poses - angles = np.linspace(0, 2 * np.pi, n_angles) - grasp_poses = augment_grasp_poses(grasp_poses, angles) - print(grasp_poses.shape, closing_dists.shape) - grasp_poses = np.reshape(grasp_poses, [-1, 4, 4]) - closing_dists = np.repeat(closing_dists, n_angles, axis=0) - - # Load gripper urdf - gripper = RobotWrapper.loadFromURDF(str(gripper_urdf), floating=True) - gripper.initCollisionPairs() - gripper.removeCollisionPairsFromSRDF(str(gripper_srdf)) - gripper.addOctree(points, octree_resolution) - - if add_ground: - gripper.addBox( - [1, 1, octree_resolution], - toSE3([0, 0, min_bound[2] - octree_resolution * 2]), - name="ground", - ) - - if vis: - gripper.addMeshVisual(np.array(mesh.vertices), np.array(mesh.triangles)) - - # TCP frame - tcp_frame = gripper.get_frame(tcp_link_name) - root_to_tcp = np.array(tcp_frame.placement.inverse()) - - # 4. Search - valid_grasp_poses = [] - valid_grasp_qpos = [] - pbar = tqdm.tqdm(total=len(grasp_poses)) - if open_widths is None: - open_widths = [0.01, 0.02, 0.03, 0.04] - - for grasp_pose, closing_dist in zip(grasp_poses, closing_dists): - root_pose = grasp_pose @ root_to_tcp - pos = root_pose[:3, 3] - quat = Rotation.from_matrix(root_pose[:3, :3]).as_quat() - for w in open_widths: - if w < closing_dist: - continue - grasp_qpos = np.hstack([pos, quat, w, w]) - if gripper.isCollisionFree(grasp_qpos): - valid_grasp_qpos.append(grasp_qpos) - # sapien format - pq = np.hstack( - [ - grasp_pose[:3, 3], - transforms3d.quaternions.mat2quat(grasp_pose[:3, :3]), - ] - ) - valid_grasp_poses.append(np.hstack([pq, w])) - break - pbar.update() - - valid_grasp_poses = np.array(valid_grasp_poses) - valid_grasp_qpos = np.array(valid_grasp_qpos) - print("#valid grasp poses", len(valid_grasp_poses)) - - if len(valid_grasp_poses) > 0 and vis: - gripper.initMeshcatDisplay(None) - gripper.play(valid_grasp_qpos, dt=0.5) - - return valid_grasp_poses - - -def main(): - from mani_skill import DESCRIPTION_DIR - from mani_skill.envs.pick_and_place.pick_single import YCB_DIR - - # Load shape - # mesh_path = str(YCB_DIR / "models/002_master_chef_can/convex.obj") - # mesh_path = str(YCB_DIR / "models/065-h_cups/convex.obj") - mesh_path = str(YCB_DIR / "models/072-a_toy_airplane/convex.obj") - mesh = o3d.io.read_triangle_mesh(mesh_path) - mesh.compute_vertex_normals() - # Open3d interpolates normals for sampled points. - pcd = mesh.sample_points_uniformly(number_of_points=2048, seed=0) - points, normals = np.array(pcd.points), np.array(pcd.normals) - - # vis_pcd = o3d.geometry.PointCloud() - # vis_pcd.points = o3d.utility.Vector3dVector(points) - # vis_pcd.normals = o3d.utility.Vector3dVector(normals) - # o3d.visualization.draw_geometries([vis_pcd]) - - # 1. Generate contact point pairs - row, col, score = compute_antipodal_contact_points( - points, normals, 0.08 * 0.95, 0.97 - ) - print("#contact points", len(row)) - - # 2. Build initial grasp poses - grasp_poses, closing_dists = initialize_grasp_poses(points, row, col) - - # 3. Search feasible grasp poses - n_angles = 36 - angles = np.linspace(0, 2 * np.pi, n_angles) - grasp_poses = augment_grasp_poses(grasp_poses, angles) - print(grasp_poses.shape) - grasp_poses = np.reshape(grasp_poses, [-1, 4, 4]) - closing_dists = np.repeat(closing_dists, n_angles, axis=0) - - gripper_urdf = str(DESCRIPTION_DIR / "panda_v2_gripper.urdf") - gripper_srdf = str(DESCRIPTION_DIR / "panda_v2.srdf") - gripper = RobotWrapper.loadFromURDF(gripper_urdf, floating=True) - # print(gripper.nq, gripper.nv) - # print(gripper.model.names) - # print(gripper.model.frames) - - gripper.initCollisionPairs() - gripper.removeCollisionPairsFromSRDF(gripper_srdf) - gripper.addOctree(points, 0.001) - gripper.addMeshVisual(np.array(mesh.vertices), np.array(mesh.triangles)) - # # Add a ground - # gripper.addBox([1, 1, 0.005], toSE3([0, 0, -0.005]), name="ground") - - # TCP frame - tcp_frame = gripper.get_frame("panda_hand_tcp") - root_to_tcp = np.array(tcp_frame.placement.inverse()) - - # 4. Search - valid_grasp_poses = [] - pbar = tqdm.tqdm(total=len(grasp_poses)) - w_step = 0.005 - widths = np.arange(0.01, 0.04 + w_step * 0.5, w_step) - - for grasp_pose, closing_dist in zip(grasp_poses, closing_dists): - root_pose = grasp_pose @ root_to_tcp - pos = root_pose[:3, 3] - quat = Rotation.from_matrix(root_pose[:3, :3]).as_quat() - for w in widths: - if w < closing_dist: - continue - grasp_qpos = np.hstack([pos, quat, w, w]) - if gripper.isCollisionFree(grasp_qpos): - valid_grasp_poses.append(grasp_qpos) - break - pbar.update() - - valid_grasp_poses = np.array(valid_grasp_poses) - print("#valid grasp poses", len(valid_grasp_poses)) - - if len(valid_grasp_poses) == 0: - exit(0) - - # Visualize - gripper.initMeshcatDisplay(None) - gripper.play(np.array(valid_grasp_poses).T, dt=0.5) - - -if __name__ == "__main__": - main() diff --git a/mani_skill/utils/building/assets/floor_tiles_06_2k.glb b/mani_skill/utils/building/assets/floor_tiles_06_2k.glb deleted file mode 100644 index 56cbbfc11ee3ff937480a56ae821e066b136b441..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 17487172 zcmd?QcUY6nx-X2Ppdg?kA|PErLFv6kq^U>^y^1uc0YYyPk*Y`&A~gsq9TcQDL3#^F zlNtym5Nc>iAS5UHzH9IG?X|wM&;H~4=X|-C%*;J=x8Hrw%=2(foZJjG&QefNsEyN{ z`A$Va@ffJ96U<`^20Qrh$OrQ{Ie0jD+xmdKdE|Mto_m8lKwvH>G7HxOHwO=U2XC%N zBD!3DlER|Gl9D`kdHfu_!OkEL@?>#gQJ&DdJPrXq4j#WrU?Z@DJ&(KvkJdv28y_;+ z*W1Cy$J^Ee>*-!cQTC!_-E`c zj|a%!;rD>3RVZ1(KYGDD?hfGRe^~#Xujgh-jpSd{zo$Y_y=i}|`Kod@!vRb-GnnU@E!aU7~Vqx%Q;|A@yw@b&a| zc6au1_WOI(*2l-&`I)cJ?`|-Uo-Xi_;Uit0KY`KFHPBXh!Xqzsm&Z`uR8?2kK+Q&! zM_&9d@15;_vq+Ft{T-U$VEl>I-2Kqn`8ql}cpE!A`18n1k~KNII~WFf{?4vHlSDi{oPLM+--X_W9C#Z^S91Q7 z1MyE{r2dT<7f*-(cRBXXj*d3sWI<%pZ0*QSgF)VZx%SUu$==z4+&w`a4jw*#nJ6VD zF8!P6>p@<`zm2ygr;CNCu-u=ItgNVvoT!YLsN`MIyZ=i39YJUE61KSauVZ9}e?uj6 z`1}QpvAU|nzqn0~;eTV_|BhSyf50vNFS^M&@~^u85dUC9M(JG!@VAt@(KPSt-z|G#DZE$aW2 zSAq=4KdY7d?Zf|}Ua|keD<=M56ifVXj24p?l@pbaA*a(ns~3~}v!edO>|Y!elaP~? zmHRJ1{ShQ3CjC3B|6YlI0Zm?f8qRLy&6Y=APDWhrPaggUK~7fYH^X0w{tnDPMMF$R zQdUaxx6n|o=R-7S9z4>aAd(=hp#)|&?qQg{GRvX_uT)!o%o%C z;>-R2EWi8zp#84TP*74(kjq($SUnw0IvQ3Qa+mJWLp1{miVNiC1&Yh$GnWD>!vLnB zI16+zedMUEO(95TM1h>0K|Uam8yAnMyrre*b2~SXuf3(E9>^__>!Gcmg9n$&??W1F zY5DgSPi~R-Buh(iS4+$P=TY=u`+xQ)aaZBrTNu~9d%qWmH+gR%pVD0R{ zr49k!{}ZL;={yv=o(>+5e($x9bh*TZ#e~JAx#YS2ulAVY49g>OCanEOeeR4HIV~tC z_Q+-B3@gQXa(#yUH+hEbKiY_BXNq%w(a*AzYYK{Ui4+vnziUx(3DKl8f3?q?rKCt^ z{3C6SJa!@XA9KV+Mag}Nw1kM}@||CbW}>mE!N+7H&c)Mc8BGXu-`jA&aG1JgwPn+I>C~#PI>Yq4eBLk zHa{(`H>3PEF9sSoHnuAfBi%WPt9JVKYce=M=k#6VO5ntDpAa4GVH{jAceg2Vu11FR&5uVeC;StX9+qGu;@Bau$7B`r?&5zVmch9*io zOs<>W7W=hn1zjTQCHJ%CNk)4e5B#D(CEc7oeNSa;)R7Q{ic|dgr2KH!r2}R^f1{%G z+_%iZ;S+NaHXLj~s9r#(s-;VX+x};5jHrRbSS3>WAScb{@_D1CfVC3A#_HuO)LIeS ztnLfDh83=#pQY!wO?A+3C z{ek5tO|=0c0o>tsIRpXA5*uNoD(*#>j_i(zLPu!RV6}&#_VpV71NDt4Di#ofhYHkB zda$2Ps0$Ql6FXDGeRGl=p|R4}JEmTW1Ecb|lB<)VC4X%UUSj0rbT$e|lq(fwOcz~ybGxO)n^(Ov`7<%#r{xXXOX8S^S$tK>!i`!^ zHZircOr*7}_w*vTNIw9N?#2NlD-u4;Fwu=M*_|G_hBk>X>0Fe%+Q4p<2xJf#;v(8N ztv}9s6zUrdGu#y`q}|(EnjM}V70{>a2bW>p-L$`YcQYtz9}lKa8jLG$HpKR|Kk`pV zT&Gx^G0(U-xGuLjl~C@h2K;WHMXMSnZDC%5i;THyDaIs{DK&*LWPKh34^S0R@N$}4 z+(dnT468vi>yBR`=FRWmjIS+r!g)S|Ag)G7x{$|d9gTVV320L&+T3-Pty!Uf|6#aHwv@97{tihzs* zA=V9p_cCJ%7OzA4D0ROIHuZvOXZgfv0SU5MCrY~q<07U;gH5%Hj0AJ~?%6dFu^CO# znVoYnl9CzoqfWsu|&-cBy+zuagzL7P`I61}Q+Uq`7PxjVZ|L%&4V z>@6V`)v|rT^8l$ET7e9)rX`{wB_-w9t~j5?FZ>}{3OTcz6|LImq`4$~TnPG)>rRKLiGeBm;8xtZp`+ z=cFY)dUXSYWo4J|EloLjI+qS(%|25EFyfx*?>*PYcrGuG78S;nv%DbGAw&xoOSpdA z*5|(pFXO|F{FHnyacsX(cD3LIqB5VKI4xtLlz}vWm$PK$^Ae;sGtrTkI#KqRnqAR? zN4f{OuH(+_xTiUV->JQh+k=mI2_Nn~zhuqZ%JZ#b2Q^Gn0=b3idTtx0tzEaKYSOVR zw9~o+Z;zcGEXAx^rB7Df(sEd=>Wh6v)dUPuW*%C8gOyx{`GWYba%}Hf+~W9Q`4D+} zH68r1pAuFv+?U{3!WoeFgg_@hPFFVkv%6+bmgYgq+{mZi2NF!t0hhr{j}#9qe&_@_ zT@RWM6EIm)EX?R`xZaBiJd=}!AC}jyW9c4K%%S3XfB-W8l#O1jclee8%?xNFFK9@t ze5ID4#QpS<_9fOPp{qDo+NCl)8V*g$D zB#P3v#Gti>(-mG8)RDb%wqh;A_=RjLjnv-r$q(`lT0gjqyCa~! z2M@-J?T>qSbvr@0Eun<`yTM0+v0>6i}toNl)_1XEn%4JU#t0AUs^f_sk#3N^w zO>A+h;7<^8r#774s8w?g5PQCt1(GV-<%LD6&G z$y;r2rr<_FlMwI64{!FKw(9I{S>QSW=9_)`9cH9Eq_JQ@;R5?#USXFQ=`?bywq7TU z-Ljzy^o)jmR`ghzW{N)aEbj_d8^>**M8|aiQH=5Xik7k(IlW0Iaf}R$s#KI60Q8ih^DHbRO;YTD$P78`uTrGx@KtD6&}3pcgj zl&2#Xijb2pVV{F4Ie5B*?V9Y%D|PAZl>I1`4Nxuz*|eabP6duSEf*?2DDx_O+~3J! zV{q*f$;yZ~;VZPKVkOAOp*Q8TC`3b2b+qorNl2&2SVeJm zI5@DfUu_s}>brQ8^qUn^d6$qU>dYv-`8Di~`ffsU(wP6x zs|Deu;hJfTo!@C#Zr+pI$6Yy}5svsO-e^j%SX;Aam;HkmZWj2ZtVBLeS2-dx!=i)J znMZ$TTr(?0iFov!uC{Hx3yhWb~es8D^tvh8p+ z;P}(MCTvdeL6)lOT{iq$xvhz0!lva$4P7U~=>bpqj>h>nJ0@-CaxzLb*J~a1ge|F# z+n$dG1W4%sCfOT{`se#TTuAeY94(+Mv(n5#D*6}Wl-E0YwNcvn3#}5W-u>|8}kC3tORAJqojzTRXpy4(tZ((n0$qa_aIa_g^{Ps#Zh+#JoNQfO;>uS- z*J$1gP2Y$B4_}OAQI4T4mwhX29}m&eq8$%UbM8 zbdXtz*!gXh&eln*Eay1}=)vr@!3GU4(;W>p1RgD&gSBKmPK>ZTnpCc{`a=$Z#^ zhUFpoTHqd3m|)aGmeUpNbPAtIPO!{vG$jhlV--*cPwZxl*Cs$`I3t6r4zw^9jcYQU zT|4*qCGIjGd$!Nfw7mZ`M1lPXR9|s3FZdy>O&qNox&$E1UwXzVT{av1rYmGGg?RGy z0Dk)OBWnMJD=~O9O6M)9w-&=rfP85U!PzuUkw#ii76H5@o1=LM@k7ZFNZF2Rf7g0o zey9Exj%3wS0vrU~bgx)m35l>@ART*d<@$H!BuA837_}fiZQLRB!f@Obx;4UzA;J

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