Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement

Key points

• Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time.1
• We therefore propose Code-Only-as-Policy (COAP): code measures and tracks the robot, environment, and task state from camera images and proprioception, and makes every decision from it.1
• These advantages make COAP a suitable medium for recursive self-improvement (RSI): coding agents develop the library in a closed loop, and each change is explicit and controllable.1
• We thus propose COAP as a new paradigm for embodied tasks; since it applies across episodes, it can also serve as an efficient data engine for VLAs and Agent Harnesses.1

Sources (2)

• [1]Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement
  Hugging Face Daily Papers · Oct 8, 12:00 AM

  We propose a different view: the embodied world is an Embodied Turing Machine, whose tape is the robot and environment state and rules are the policy.

  Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time.

• [2]Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:29 PM · same content

Extractive summary: sentences quoted from the sources.