Skill-Space Shooting for Autonomous Robot Policy Improvement
This paper introduces 'skill-space shooting,' a method for robots to autonomously improve their task policies by leveraging foundation models to identify and correct failures using reusable 'skills.' It aims to reduce reliance on human intervention for policy refinement in complex, real-world environments.
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This paper introduces 'skill-space shooting,' a method for robots to autonomously improve their task policies by leveraging foundation models to identify and correct failures using reusable 'skills.' It aims to reduce reliance on human intervention for policy refinement in complex, real-world environments.
Imagine a robot learning to play darts. Instead of a human constantly adjusting its aim after every miss, the robot uses its 'brain' (a foundation model) to understand why it missed (e.g., 'threw too hard,' 'aimed too low'). It then tries a small, pre-learned 'skill' (like 'adjust throw strength' or 'raise arm angle') to correct its mistake, improving its overall dart-throwing policy without needing a human to show it every single correction.
How Skill-Space Shooting Works
- 11. A robot policy attempts a task and encounters a failure.
- 22. A foundation model analyzes the failure and suggests potential corrective 'skills' from a library of reusable behaviors.
- 33. The robot executes these suggested skills in a 'shooting' phase to explore effective corrections.
- 44. Successful corrections are then integrated back into the robot's main policy, improving its performance for future attempts.
- 55. These learned skills can also be shared across different tasks, reducing the effort needed to teach new improvements.
Pₙew = P_old + ΔP(s_corr)
P_new represents the improved policy, P_old is the current policy, and ΔP(s_corr) signifies the change in policy derived from a successful skill-based correction (s_corr).
A robot arm tasked with assembling a complex object might fail to pick up a specific part. Instead of a human reprogramming it, a foundation model identifies the failure as 'incorrect grip force.' The robot then autonomously tries different 'grip force adjustment' skills until it successfully picks up the part, integrating this successful correction into its assembly policy.
Autonomous policy improvement in robots can be achieved by using foundation models to guide the exploration and integration of reusable 'skills' as corrective actions, significantly reducing human supervision.