Recursive Harness Distillation across Agents for Robot Manipulation
A central goal in robotics is to enable manipulation across changing tasks and environments. Vision-language-action (VLA) models provide broad manipulation capabilities but can struggle when execution requires diagnosing failures and adapting behavior. Strong agents can discover effective interventions through interaction with these policies. We propose Recursive Harness Distillation to accumulate this experience as reusable guidance across agents. A strong agent distills its experience into a playbook for a light agent, then recursively refines the playbook using the light agent's execution feedback. The resulting playbook enables agents to reuse accumulated intervention knowledge in new...
| Authors | Seungyeon Kim, Junhoo Lee, Minkyu Kim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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