robotics
Minutes of Play Seed Hours of Robot Practice
DSP clones a brief human play policy, lets the robot collect its own interaction data and uses that policy to guide model-predictive control.
Summary
DSP clones a brief human play policy, lets the robot collect its own interaction data and uses that policy to guide model-predictive control.
Contact-rich robot learning needs experience, but collecting it through task-specific demonstrations or reinforcement learning can be expensive. Dexterity from Supervised Play starts with minutes of human play, clones that behavior and then deploys it to gather hours of autonomous interaction for a world model. The same play policy proposes candidate actions for model-predictive control, and a distilled policy can learn through self-play inside the model. On real underactuated pushing and in-hand reorientation, the authors report a 69.6 percent success-rate improvement over hindsight behavior cloning and performance comparable to reinforcement learning with 490 times less interaction data. Those results are task-specific, not a claim of general robot autonomy.
Why it matters
DSP clones a brief human play policy, lets the robot collect its own interaction data and uses that policy to guide model-predictive control.
Limits and context
- Those results are task-specific, not a claim of general robot autonomy.
Key claims
DSP clones a brief human play policy, lets the robot collect its own interaction data and uses that policy to guide model-predictive control.
Qualification: Those results are task-specific, not a claim of general robot autonomy.
Evidence: source-2026-10-16-014
Sources
- arXiv preprint 2610.22340, original v1arXiv / submitting research authors · primary research
Corrections
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