DA-GRD：面向感知-执行失配的决策感知抓取相关消歧，用于触觉恢复
DA-GRD: Decision-Aware Grasp-Relevant Disambiguation for tactile recovery under perception-to-execution mismatches
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中文总结 AI 辅助
针对感知-执行失配下的抓取姿态失效问题，提出决策感知的触觉消歧方法DA-GRD，通过加权平面信念与选择性触觉探测，在无视觉条件下以少量交互高效恢复可执行抓取，显著提升成功率。
中文摘要 AI 辅助
抓取是连接感知与物理任务执行的基本机器人能力。本文研究在感知-执行失配下的抓取姿态恢复问题，即当物体在执行前发生移动时，由视觉感知生成的抓取可能在空间上失效，且仅依赖稀疏的触觉交互而无进一步视觉观测。我们提出DA-GRD（决策感知抓取相关消歧），该方法维护关于可能物体配置的加权平面信念，并根据触觉探测消除假设及提升候选任务抓取间一致性的能力来选择触觉探测。不同于完全重新定位物体，DA-GRD在剩余假设支持一个共同可执行抓取时即停止。在MuJoCo实验中，针对十个刚性物体，平移达5厘米、偏航扰动达±45°，DA-GRD实现了84.7%的物理提升成功率，而陈旧的AnyGrasp为9.1%，原始固定扫描基线为21.2%，采用SE(2)信念的固定扫描方法为63.7%。DA-GRD还达到了57.3%的任务条件成功率。跨物体而言，在十个每物体均值上，其成功平均使用4.13次触觉探测，相对于固定15次探测的基线减少了72.5%。真实世界实验在六个物体上实现了71.7%的物理提升成功率和38.3%的任务条件成功率，平均使用4.20次探测。这些结果表明，在视觉缺失条件下，触觉感知能够通过有限的物理交互恢复任务相关抓取，而无需完整物体定位。
英文摘要
Grasping is a fundamental robotic capability that bridges perception and physical task execution. This paper studies grasp pose recovery under a perception-to-execution mismatch, where a grasp generated from visual perception may become spatially stale if the object moves before execution, using only sparse tactile interactions and no further visual observations. We propose DA-GRD, Decision-Aware Grasp-Relevant Disambiguation, which maintains a weighted planar belief over possible object configurations and selects tactile probes according to their ability to eliminate hypotheses and improve agreement among candidate task grasps. Rather than fully relocalizing the object, DA-GRD stops when the remaining hypotheses support a common executable grasp. In MuJoCo experiments on ten rigid objects with translations up to 5~cm and yaw perturbations up to $\pm45^\circ$, DA-GRD achieves an 84.7% physical lift success rate, compared with 9.1% for stale AnyGrasp, 21.2% for the original fix-scan baseline, and 63.7% for fix-scan method adapted with an SE(2) belief. DA-GRD also achieves a 57.3% Task conditioned Success rate. Across objects, it uses a success-average of 4.13 tactile probes over the ten per-object means, corresponding to a 72.5% reduction relative to the fixed 15-probe baselines. Real-world experiments on six objects achieve 71.7% physical lift success and 38.3% task-conditioned success with 4.20 probes on average. These results show that tactile sensing can recover task-relevant grasps under vision-off conditions with limited physical interaction, without requiring complete object localization.
发表机构
- Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（先进智能与计算研究所（IAIC），新加坡科技研究局（A*STAR））
- Nanyang Technological University（南洋理工大学）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
