功能导向的软机器人策略的虚拟人演示重定向
Functional Force-Aware Retargeting from Virtual Human Demos to Soft Robot Policies
AI总结：
本文提出SoftAct框架，通过显式考虑接触力来教软机器人手执行类人操作技能。采用虚拟现实技术捕获丰富的示范数据，结合两阶段力感知重定向算法，实现力平衡映射和实时接触优化，提升软机器人在复杂任务中的表现。
摘要：
我们介绍了SoftAct，一种框架，用于通过显式推理接触力来教授软机器人手执行类人操作技能。利用沉浸式虚拟现实，我们的系统捕获了丰富的真人示范，包括手运动学、物体运动、密集接触区域和详细接触力信息。不同于传统方法直接重定向人类关节轨迹，SoftAct采用两阶段、力感知重定向算法。第一阶段将示范的接触力分配给个体手指并按比例分配给机器人手指，建立人与机器人手之间的力平衡映射。第二阶段通过结合基线末端执行器姿态跟踪与测地线加权接触细化，利用接触几何和力的大小实时调整机器人指尖目标。这种公式使软机器人手能够再现人类示范的功能意图，同时自然适应极端身体匹配不匹配和非线性刚度。我们在一组接触丰富的操作任务上评估了SoftAct，使用定制的非人形气动软机器人手。SoftAct的控制器将指尖轨迹跟踪RMSE减少高达55个百分点，比运动学和学习基线减少跟踪方差高达69个百分点。在策略层面，SoftAct在零样本现实世界部署和仿真中均表现出更高的成功率。这些结果表明，显式建模接触几何和力分布对于有效转移技能到软机器人手至关重要，无法仅通过运动学模仿恢复。项目视频和额外细节可在https://soft-act.github.io/获取。
英文摘要：
We introduce SoftAct, a framework for teaching soft robot hands to perform human-like manipulation skills by explicitly reasoning about contact forces. Leveraging immersive virtual reality, our system captures rich human demonstrations, including hand kinematics, object motion, dense contact patches, and detailed contact force information. Unlike conventional approaches that retarget human joint trajectories, SoftAct employs a two-stage, force-aware retargeting algorithm. The first stage attributes demonstrated contact forces to individual human fingers and allocates robot fingers proportionally, establishing a force-balanced mapping between human and robot hands. The second stage performs online retargeting by combining baseline end-effector pose tracking with geodesic-weighted contact refinements, using contact geometry and force magnitude to adjust robot fingertip targets in real time. This formulation enables soft robotic hands to reproduce the functional intent of human demonstrations while naturally accommodating extreme embodiment mismatch and nonlinear compliance. We evaluate SoftAct on a suite of contact-rich manipulation tasks using a custom non-anthropomorphic pneumatic soft robot hand. SoftAct's controller reduces fingertip trajectory tracking RMSE by up to 55 percent and reduces tracking variance by up to 69 percent compared to kinematic and learning-based baselines. At the policy level, SoftAct achieves consistently higher success in zero-shot real-world deployment and in simulation. These results demonstrate that explicitly modeling contact geometry and force distribution is essential for effective skill transfer to soft robotic hands, and cannot be recovered through kinematic imitation alone. Project videos and additional details are available at https://soft-act.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
