发表机构
Tongji University; Shanghai Research Institute for Intelligent Autonomous Systems; Purdue University; Nanyang Technological University, Singapore（同济大学; 上海自主智能无人系统研究院; 普渡大学; 新加坡南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DexForge通过可微分物理模拟器，结合接触感知运动学与力感知动力学重定向，将人类视频演示转换为高保真机器人轨迹，在七个灵巧手上显著提升成功率并降低跟踪误差。
AI 中文摘要
人类演示为精确的灵巧操作提供了丰富的示例，也是机器人训练数据的有前景来源。然而，在不同机器人实体上高保真复现演示动作和手-物交互，在物理约束下仍然具有挑战性。我们提出了DexForge，一个可微分的基于物理的框架，用于将人类视频演示转换为高保真机器人轨迹。我们从视觉观察中重建球面高斯物体模型和手-物运动，然后构建一个可微分模拟器，结合高效的高斯碰撞检测与现有的可微分动力学。基于该模拟器，DexForge将接触感知的运动学重定向与力感知的动力学重定向相结合：机器人适应的稳定接触指导运动学参考构建，以及随后的基于梯度的控制细化，以实现精确的物理运动复现。在七个灵巧手上的130个DexYCB和HOT3D演示上的实验显示，成功率比基线提高了约35-53个百分点，成功轨迹上的物体位置和方向跟踪误差分别减少了约34-67%和71-78%。进一步的实验展示了到MuJoCo的开环转移和真实机器人执行。我们的项目页面可在该URL获取。
英文摘要
Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reproduction of demonstrated motions and hand-object interactions across robot embodiments remains challenging under physical constraints. We present DexForge, a differentiable physics-grounded framework for converting human video demonstrations into high-fidelity robot trajectories. We reconstruct spherical-Gaussian object models and hand-object motion from visual observations, then build a differentiable simulator combining efficient Gaussian collision detection with existing differentiable dynamics. Based on this simulator, DexForge combines contact-aware kinematic retargeting with force-aware dynamics retargeting: robot-adapted stable contacts guide kinematic reference construction and subsequent gradient-based control refinement for precise physical motion reproduction. Experiments on 130 DexYCB and HOT3D demonstrations across seven dexterous hands show success-rate gains of approximately 35-53 percentage points over the baseline, with object position and orientation tracking errors on successful trajectories reduced by approximately 34-67% and 71-78%, respectively. Further experiments demonstrate open-loop transfer to MuJoCo and real-robot execution. Our project page is available at https://wmz1226.github.io/DexForge/
