Preprint: RoboRSI Self-Tests Robot Skill Reuse, Beats Baselines by 2.7–11.0 Points in Sim
New system enables robot self-evolution via hierarchical skill decomposition, achieving top success rates in multiple simulations.
ImportanceMaterialEvidenceE2 unreplicatedWrite-upQuick
Key Finding: The RoboRSI system reports success rates exceeding the strongest baselines by 2.7 to 11.0 percentage points on LIBERO and RoboTwin simulations.
Context: Generalist robots need to learn from execution experience, but traditional code repair struggles to attribute errors to specific task structures, preventing effective reuse of learned capabilities.
Result: Built on Top-Down Skill Refinement (TSR), the system decomposes tasks into compound, atomic, and base skills with explicit contracts. Coordinated agents (Manager, Planner, etc.) enable a mobile manipulator to iterate skills over 104 rounds of household cleanup tasks.
Limitation: Data comes from author-reported simulations without independent reproduction yet; generalization to physical environments remains unverified.