PaperReading

This is a Github repository that focuses on articles related to skill-based meta reinforcement learning. The main focus is on skill extraction, combination, and generalization.

1. 😊[2023.07.05]😊 Nam, Taewook, et al. "Skill-based meta-reinforcement learning." arXiv preprint arXiv:2204.11828 (2022).
2. 😊[2023.07.11]😊 Yoo, Minjong, Sangwoo Cho, and Honguk Woo. "Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning." Advances in Neural Information Processing Systems 35 (2022): 37432-37444.
3. 😊[2023.07.12]😊 Cheng, Shuo, and Danfei Xu. "Guided Skill Learning and Abstraction for Long-Horizon Manipulation." arXiv preprint arXiv:2210.12631 (2022).
4. 😊[2023.07.14]😊 Guan, Lin, et al. "Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning." arXiv preprint arXiv:2305.14909 (2023).
5. 😊[2023.07.16]😊 Chen, Yongchao, et al. "AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers." arXiv preprint arXiv:2306.06531 (2023).
6. 😊[2023.07.21]😊 Myself. "RL-and-Variational-Inference-v2.pdf"
7. 😊[2023.07.26]😊 Myself. "SAC_logprob.md"

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1. 🥰[2023.08.16]🥰 Huang, Wenlong, et al. "Language models as zero-shot planners: Extracting actionable knowledge for embodied agents." International Conference on Machine Learning. PMLR, 2022.
2. 🥰[2023.08.17]🥰 Ding, Yan, et al. "Task and motion planning with large language models for object rearrangement." arXiv preprint arXiv:2303.06247 (2023).
3. 🥰[2023.08.17]🥰 Cao, Yue, and C. S. Lee. "Ground Manipulator Primitive Tasks to Executable Actions using Large Language Models." arXiv preprint arXiv:2308.06810 (2023).
4. 🥰[2023.08.18]🥰 Ding, Yan, et al. "Robot task planning and situation handling in open worlds." arXiv preprint arXiv:2210.01287 (2022).
5. 🥰[2023.08.18]🥰 Perez, Julien, et al. "LARG, Language-based Automatic Reward and Goal Generation." arXiv preprint arXiv:2306.10985 (2023).
6. 🥰[2023.08.18]🥰 Colas, Cédric, et al. "Language-conditioned goal generation: a new approach to language grounding for RL." arXiv preprint arXiv:2006.07043 (2020).
7. 🥰[2023.08.21]🥰 Eysenbach, Benjamin, et al. "Diversity is all you need: Learning skills without a reward function." arXiv preprint arXiv:1802.06070 (2018).

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1. 😘[2023.09.25]😘 Vemprala, Sai, et al. "Chatgpt for robotics: Design principles and model abilities." Microsoft Auton. Syst. Robot. Res 2 (2023): 20.
2. 😘[2023.09.26]😘 Liu, Haokun, et al. "LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks." arXiv preprint arXiv:2308.14972 (2023).

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1. 🤗[2023.10.02]🤗 Elhafsi, A., Sinha, R., Agia, C., Schmerling, E., Nesnas, I., and Pavone, M., “Semantic Anomaly Detection with Large Language Models”, arXiv e-prints, 2023. doi:10.48550/arXiv.2305.11307.
2. 🤗[2023.10.06]🤗 Liang, J., “Code as Policies: Language Model Programs for Embodied Control”, arXiv e-prints, 2022. doi:10.48550/arXiv.2209.07753.
3. 🤗[2023.10.09]🤗 Jin, E., “Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI”, arXiv e-prints, 2023. doi:10.48550/arXiv.2310.01824.
4. 🤗[2023.10.15]🤗 Zhou, Haoyu, et al. "Generalizable Long-Horizon Manipulations with Large Language Models." arXiv preprint arXiv:2310.02264 (2023).