Physical Agency Orchestrator Significantly Improves Generalist Robot Task Performance Without Retraining
A new arXiv preprint introduces Pigey, a closed-loop orchestrator that decomposes complex robotic tasks into subgoals, directs low-level policies, and recovers from failures, all without additional training. In experiments, Pigey improved performance on the LIBERO-PRO benchmark from 12.8% to 53.3% and increased real-world policy success rates from near-zero to over 90% on tasks requiring reasoning, using existing vision-language-action policies. The approach highlights the potential of separating high-level planning from low-level control in generalist robots.
Why it matters: This result suggests a practical path to dramatically improving generalist robot performance without retraining, which could accelerate real-world deployment and flexibility of robotic systems.
Full story at: arXiv Robotics ↗