外观
论文arXiv 2609.290311 个月前
面向直驱夹爪零样本 Sim-to-Real 抓取的简单力矩观测对齐
原文链接：Simple Torque-Observation Alignment for Zero-Shot Sim-to-Real Grasping with a Direct-Drive Gripper（arXiv 2609.29031 · 发布日期：2026-09-24）
Simple Torque-Observation Alignment for Zero-Shot Sim-to-Real Grasping with a Direct-Drive Gripper
针对仿真与实测力矩不匹配，提出力矩观测对齐方法，并在多指直驱夹爪零样本抓取中验证。
核心要点
- 力矩观测因尺度、偏置和噪声差异，影响强化学习策略从仿真迁移到现实。
- 方法通过测力计标定 K_tau* 校正尺度，并用力矩差分消除恒定偏置。
- 训练时注入由测力计数据得到的高斯噪声，策略仅用关节位置和力矩差分抓取。
- 在九个分布内物体消融中，该方法达到100%抓取成功率并提升鲁棒性。
验证范围与局限：验证范围为完全仿真训练、部署到多指DD夹爪，并在九个分布内物体上评估。
英文摘要 (Abstract)
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.
作者：Doyoung Kim, Edgar Lee, Hyeonsun Park, Chunghyeon Lee, Chihyun Han, Hwang, Uisu, Seokhwan Jeong
来源
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