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From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data
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While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: https://play-to-policy.github.io
Forward citations
Cited by 8 Pith papers
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Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents
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Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Action chunking in robotic behavioral cloning works mainly because it acts as a delayed-prediction policy and an implicit ensemble, not because of temporal consistency or horizon reduction.
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Spinning Straw into Gold: Relabeling LLM Agent Trajectories in Hindsight for Successful Demonstrations
Relabeling LLM-agent trajectories with all goals actually achieved, plus action masking and reweighting, yields sample-efficient gains over SFT and DPO on ALFWorld, PlanCraft, and WebShop.
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Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
MPAIL2 demonstrates real-world manipulation learning from observation alone, without rewards or action labels, plus positive online transfer.
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Behavioral Exploration: Learning to Explore via In-Context Adaptation
A coverage-conditioned behavioral cloning policy adapts in-context to its own history, making robots explore new expert-like behaviors online without online reinforcement learning.
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VLM-TDP: VLM-guided Trajectory-conditioned Diffusion Policy for Robust Long-Horizon Manipulation
VLM-TDP guides a diffusion-based robot policy with VLM-generated voxel trajectories, improving success rates by roughly 30-44% and adding robustness to noise and scene changes.
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Fast-in-Slow: A Dual-System Foundation Model Unifying Fast Manipulation within Slow Reasoning
FiS-VLA embeds a diffusion-based action module into the final transformer blocks of a vision-language model, achieving 69% mean success on RLBench and a claimed 117.7 Hz control frequency.
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