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Effective task representations should facilitate compositionality, such that after learning a variety of basic tasks, an agent can perform compound tasks consisting of multiple steps simply by composing the representations of the constituent steps together.
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Lashley, K.S · 1951
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Kaelbling, L.P · 1993
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Where Does Compositionality Come From?
Steedman, M · 2004
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Fitted Q-Iteration by Advantage Weighted Regression
Neumann, G. and Peters, J · 2008
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Transitive Inference in Stimulus Equivalence and Serial Learning
Dickins, D.W · 2011
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Kulkarni, T.D., Narasimhan, K., Saeedi, A., and Tenenbaum, J · 2016
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Successor Features for Transfer in Reinforcement Learning
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Learning to Act by Predicting the Future
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Goal-Conditioned Imitation Learning
Ding, Y., Florensa, C., Abbeel, P., and Phielipp, M · 2019
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Dynamics Learning With Cascaded Variational Inference for Multi-Step Manipulation
Fang, K., Zhu, Y., Garg, A., Savarese, S., and Fei-Fei, L · 2019
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Learning Latent Dynamics for Planning From Pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2019
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Human Few-Shot Learning of Compositional Instructions
Lake, B.M., Linzen, T., and Baroni, M · 2019
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Planning With Goal-Conditioned Policies
Nasiriany, S., Pong, V.H., Lin, S., and Levine, S · 2019
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Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Peng, X.B., Kumar, A., Zhang, G., and Levine, S · 2019
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Hippocampal Contributions to Model-Based Planning and Spatial Memory
Vikbladh, O.M., Meager, M.R., King, J., Blackmon, K., Devinsky, O., Shohamy, D., Burgess, N., and Daw, N.D · 2019
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Learning to Recombine and Resample Data for Compositional Generalization
Akyürek, E., Akyürek, A.F., and Andreas, J · 2021
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Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
Blier, L., Tallec, C., and Ollivier, Y · 2021
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Decision Transformer: Reinforcement Learning via Sequence Modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
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Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
Choi, J., Sharma, A., Lee, H., Levine, S., and Gu, S.S · 2021
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BC-Z: Zero-Shot Task Generalization With Robotic Imitation Learning
Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., and Finn, C · 2021
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Should I Run Offline Reinforcement Learning or Behavioral Cloning?
Kumar, A., Hong, J., Singh, A., and Levine, S · 2021
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Language Conditioned Imitation Learning Over Unstructured Data
Lynch, C. and Sermanet, P · 2021
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Learning to Generalize Across Long-Horizon Tasks From Human Demonstrations
Mandlekar, A., Xu, D., Mart1́3n-Mart1́3n, R., Savarese, S., and Fei-Fei, L · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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Online and Offline Reinforcement Learning by Planning With a Learned Model
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No, to the Right: Online Language Corrections for Robotic Manipulation via Shared Autonomy
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Closing the Gap Between TD Learning and Supervised Learning - a Generalisation Point of View
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VIMA: General Robot Manipulation With Multimodal Prompts
Jiang, Y., Gupta, A., Zhang, Z., Wang, G., Dou, Y., Chen, Y., Fei-Fei, L., Anandkumar, A., Zhu, Y., and Fan, L · 2023
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Language-Driven Representation Learning for Robotics
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Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials
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CLIPort: What and Where Pathways for Robotic Manipulation
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Learning Invariant Representations for Reinforcement Learning Without Reconstruction
Zhang, A., McAllister, R., Calandra, R., Gal, Y., and Levine, S · 2021
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Do as I Can, Not as I Say: Grounding Language in Robotic Affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., et al · 2022
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See, Plan, Predict: Language-Guided Cognitive Planning With Video Prediction
Attarian, M., Gupta, A., Zhou, Z., Yu, W., Gilitschenski, I., and Garg, A · 2022
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Asymmetric Reinforcement Learning Facilitates Human Inference of Transitive Relations
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Symbols and Mental Programs: A Hypothesis About Human Singularity
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ProgPrompt: Generating Situated Robot Task Plans Using Large Language Models
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BridgeData V2: A Dataset for Conference on Robot Learning at Scale
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Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
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Octo: An Open-Source Generalist Robot Policy
Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., et al · 2024
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The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Laidlaw, C., Zhu, B., Russell, S., and Dragan, A · 2024
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Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
Lewis, M., Nayak, N.V., Yu, P., Yu, Q., Merullo, J., Bach, S.H., and Pavlick, E · 2024
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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Horizon Generalization in Reinforcement Learning
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OGBench: Benchmarking Offline Goal-Conditioned RL
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