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Don't Yell at Your Robot: Physical Correction as the Collaborative Interface for Language Model Powered Robots

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arxiv 2412.12602 v1 pith:7G2SOYCK submitted 2024-12-17 cs.RO cs.HC

Don't Yell at Your Robot: Physical Correction as the Collaborative Interface for Language Model Powered Robots

classification cs.RO cs.HC
keywords languagephysicalpoweredcommandscorrectionhuman-robotinteractionsinterface
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a novel approach for enhancing human-robot collaboration using physical interactions for real-time error correction of large language model (LLM) powered robots. Unlike other methods that rely on verbal or text commands, the robot leverages an LLM to proactively executes 6 DoF linear Dynamical System (DS) commands using a description of the scene in natural language. During motion, a human can provide physical corrections, used to re-estimate the desired intention, also parameterized by linear DS. This corrected DS can be converted to natural language and used as part of the prompt to improve future LLM interactions. We provide proof-of-concept result in a hybrid real+sim experiment, showcasing physical interaction as a new possibility for LLM powered human-robot interface.

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Forward citations

Cited by 2 Pith papers

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  1. Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models

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    AFIL improves VLA policy robustness by jointly training success and failure generators on online-generated failure trajectories and using adaptive guidance to avoid failure modes during action sampling.

  2. Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models

    cs.RO 2026-05 unverdicted novelty 5.0

    AFIL trains dual action generators on success and failure rollouts from a pretrained VLA to steer diffusion policies away from failure modes during inference.