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New module adapts robot torque for robust motion transfer

Researchers have developed a Torque Adaptation Module (TAM) to improve robot motion transfer across different hardware and payloads. TAM learns to adjust torque commands, enabling policies trained in simulation to perform robustly on real robots without requiring extensive retraining or domain randomization. This module has demonstrated success in zero-shot execution on a Franka Panda robot for tasks like box pushing and balancing. AI

IMPACT Enables more reliable deployment of trained robotic policies in real-world scenarios with varying hardware.

RANK_REASON The cluster contains an academic paper detailing a new method for robot motion transfer.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New module adapts robot torque for robust motion transfer

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The cluster contains an academic paper detailing a new method for robot motion transfer.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Dongwon Son, Florian Shkurti, Jason Lee, Naman Shah, Beomjoon Kim, Dieter Fox ·

    TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

    arXiv:2606.06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot. In contact-rich, dynamic manipulation, even sma…

  2. arXiv cs.AI TIER_1 English(EN) · Dieter Fox ·

    TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

    A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot. In contact-rich, dynamic manipulation, even small motion discrepancies can result in failure to t…