REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction

Zeyi Lucia Liu, Arpit Bahety, Shuran Song

REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction: 6 upvotes on Hugging Face Daily Papers, #3 of 6 papers on 2023-06-29. Day-by-day upvote history.

The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework REFLECT, which converts multi-sensory data into a hierarchical summary of robot past experiences and queries LLM with a progressive failure explanation algorithm. Conditioned on the explanation, a failure correction planner generates an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset and show that our LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Project website: https://roboreflect.github.io/

Paper page on Hugging Face · arXiv

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