The authors propose a new approach to robot control called Code-Only-as-Policy (COAP), where code measures and tracks the robot, environment, and task state, then makes decisions. COAP avoids using models in real-time and provides more efficient and flexible control.
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COAP can be useful for developing robots that can autonomously improve their skills, as it allows code to make decisions without the need for models in real-time.
The paper introduces Trace2Env, a learning-free framework that uses historical interaction traces to create a reusable environment worldbook for simulating realistic environments without rebuilding the original system. It improves next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based language world models.
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