简单扭矩观测对齐方法：用于直驱夹爪的零样本仿真到现实抓取

中文摘要 AI 辅助

在强化学习中，扭矩观测仍然具有挑战性，因为仿真扭矩和实测扭矩在尺度、偏移和噪声方面存在差异。本文针对具有直驱（DD）执行器的机器人提出了一种简单的扭矩观测对齐方法，其中电机电流通过电机类型特定的扭矩常数K_tau线性映射到关节扭矩。首先，测功机校准确定K_tau*，并修正仿真扭矩与真实扭矩之间的尺度失配。其次，该方法在两个域中使用delta_tau(t) = tau(t) - tau(t-1)作为观测值，以消除恒定偏移，而不是使用带有域相关偏差的直接扭矩tau(t)。第三，在学习过程中注入从测功机测量数据获得的高斯噪声。为验证所提方法，我们完全在仿真中训练一个教师-学生抓取策略，并将蒸馏后的学生部署到多指直驱夹爪上。部署的策略仅使用关节位置和扭矩差进行本体感觉抓取。我们在九个分布内（ID）物体上进行了消融研究，将所提方法与替代对齐变体进行比较。所提方法实现了100%的抓取成功率。这些结果表明，所提对齐方法提高了直驱夹爪上零样本策略迁移对真实世界扭矩观测失配的鲁棒性。

英文摘要

Torque observations in reinforcement learning remain challenging because simulated and measured torque differ in scale, offset, and noise. In this paper, we propose a simple torque observation alignment method for robots with direct-drive (DD) actuators, in which motor current maps linearly to joint torque through a motor-type-specific torque constant K_tau. First, dynamometer calibration identifies K_tau* and corrects the scale mismatch between simulated and real torque. Second, the method uses delta_tau(t) = tau(t) - tau(t-1) as the observation in both domains to eliminate the constant offset instead of using the direct torque tau(t), which carries a domain-dependent bias. Third, Gaussian noise obtained from the dynamometer measurement data is injected during the learning process. To validate the proposed method, we train a teacher-student grasping policy entirely in simulation and deploy the distilled student on a multifingered DD gripper. The deployed policy performs proprioceptive grasping using only joint positions and torque differences. We conduct an ablation study comparing the proposed method with alternative alignment variants on nine in-distribution (ID) objects. The proposed method achieves 100% grasp success. These results demonstrate that the proposed alignment method improves the robustness of zero-shot policy transfer on the DD gripper against real-world torque-observation mismatches.

发表机构

• Sogang University（西江大学）

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