HANDS 2026 研讨会挑战赛灵巧抓取运动赛道第二名解决方案：用于抓取运动生成的单次轨迹扭曲
2nd Place Solution to the HANDS 2026 Workshop Challenge-Dexterous Grasp Motion Track: Single-Shot Trajectory Warping for Grasp Motion Generation
- UNIST（蔚山科学技术院）
- University of Aberdeen（阿伯丁大学）
- Fogsphere (Redev AI Ltd, UK)（Fogsphere（英国Redev AI有限公司））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对灵巧抓取运动生成，提出基于单次轨迹扭曲的方法，编辑单个演示而非逐步生成，结合并行单步 PPO 训练，在 HANDS 2026 挑战赛中获得第二名，简单赛道成功率最高。
AI中文摘要：
本报告描述了我们在与 ECCV 2026 联合举办的 HANDS 2026 研讨会挑战赛（灵巧抓取运动赛道）中获得的第二名解决方案。在该挑战中，我们针对 12 自由度 LinkerHand O6 手部解决抓取运动生成问题，旨在从模拟中随机初始手部姿态出发，为未见过的物体生成物理上合理的到达和抬起轨迹。这项任务特别具有挑战性，因为每次抓取都需要一个逐步策略来做出大约 70 个十二维决策，且误差会随时间累积，而测试物体和物理动力学可能与训练时遇到的不同。为了应对这些挑战，我们提出编辑单个成功的 GraspM3 演示，而不是逐步生成运动：策略观察物体一次，并输出演示的 12 维扭曲，然后以开环方式重放。此外，我们使用单步 PPO 在所有 4,824 个训练物体上并行训练扭曲策略。最终，我们的方法在简单赛道上取得了 94.61% 的成功率，为所有提交中的最高值，在私有测试集的困难赛道上取得了 57.18% 的成功率。
英文摘要：
This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In this challenge, we address grasp motion generation for the 12-DoF LinkerHand O6, aiming to produce physically plausible reach-and-lift trajectories for unseen objects from randomized initial hand poses in simulation. This task is particularly challenging because each grasp requires a per-step policy to make approximately $70$ twelve-dimensional decisions, with errors accumulating over time, while test objects and physical dynamics may differ from those encountered during training. To address these challenges, we propose editing a single successful GraspM3 demonstration instead of generating the motion step by step: a policy observes the object once and outputs a 12-D warp of the demonstration, which is then replayed open-loop. Moreover, we train the warp policy with one-step PPO over all $4{,}824$ training objects in parallel. As a result, our method achieved success rates of $94.61\%$ on the easy track, the highest of all submissions, and $57.18\%$ on the hard track of the private test set.
