Xin Wang

The work introduces HarnessPAI, a model- and embodiment-agnostic Harness framework for Physical AI that treats code as the executable and evolvable interface organizing the underlying action primitive: within a rollout it executes open-loop with a fixed program, and across rollouts it evolves closed-loop by using execution feedback to revise the program and distill failures into reusable skills; across desktop robot arms, household robots, a robot vacuum, and a legged walking agent it improves on both pure action models and code-as-policy baselines without retraining the underlying model, with a 61.6-point gain over π0.5 on LIBERO-PRO and a 27.2-point gain over WorldDreamer on RoboCasa atomic tasks, and the converged program also serves as an expert-data collector whose data lifts π0.

The work introduces HarnessPAI, a model- and embodiment-agnostic Harness framework for Physical AI that treats code as the executable and evolvable interface organizing the underlying action primitive: within a rollout it executes open-loop with a fixed program, and across rollouts it evolves closed-loop by using execution feedback to revise the program and distill failures into reusable skills; across desktop robot arms, household robots, a robot vacuum, and a legged walking agent it improves on both pure action models and code-as-policy baselines without retraining the underlying model, with a 61.6-point gain over π0.5 on LIBERO-PRO and a 27.2-point gain over WorldDreamer on RoboCasa atomic tasks, and the converged program also serves as an expert-data collector whose data lifts π0.