提出安全可控的机器人灵巧操作框架，实现高精度与强安全性的统一。
Safe and Steerable Geometric Motion Policies for Robotic Dexterous Manipulation
- 基于几何一致性设计，将任务空间的安全约束转为配置空间的线性约束。
- 在20个物体上实现92.5%抓取成功率，三指抓取达94.4%成功率。
- 支持高层策略低维动作注入，适合需要精确控制与安全探索的场景。
灵巧操作需持续协调定义在异构几何空间中的目标与约束：机器人在ℝ⁷配置流形上控制时，需跟踪SE(3)上的末端位姿，并满足ℝ空间中的避障裕度。本文提出安全回传束动力系统（SafePBDS），一种几何一致的框架，可从任意任务流形的目标与安全要求中计算最优、可证明安全的配置流形加速度。其创新在于：一是构建回传控制屏障函数，将任务空间安全条件转化为配置空间加速度的线性约束；二是引入任务流形动作接口，使高层策略可注入低维残差运动；零输入时恢复自主行为，任意输入下仍保证安全。这使高层策略高效引导探索，而精细运动由自主行为完成。在23自由度Franka Panda-Allegro手平台仿真与实测中验证：灵巧抓取在20个日常物体上120次试验中成功率达92.5%；通过一维动作排除任一手指，三指抓取在3个物体上36次试验成功率达94.4%。该方法高效的规划与安全保障，首次实现基于模型的全驱动掌向下物体重定向，可在不同物体重量与腕部运动下实现超过360°的双向偏航旋转。演示视频与细节见：https://tml.stanford.edu/safe-pbds
原文摘要 · Abstract (English)
Robotic dexterous manipulation requires continuously reconciling objectives and constraints defined on heterogeneous geometric spaces: a robot controlled on a $\mathbb{R}^7$ configuration manifold may need to track end effector poses on $\mathrm{SE}(3)$ while satisfying obstacle avoidance margins in $\mathbb{R}$. We present Safe Pullback Bundle Dynamical Systems (SafePBDS), a geometrically consistent framework that computes optimal, certifiably safe configuration manifold accelerations from objectives and safety requirements on arbitrary task manifolds. SafePBDS builds on prior work that combines predefined task manifold dynamical systems to produce autonomous motion. Its first innovation is a pullback control barrier function construction, which converts task manifold safety conditions into linear constraints on configuration manifold accelerations. The second innovation is a task manifold action interface that allows a high-level policy to inject low dimensional residual motions; zero input recovers the autonomous behavior, while safety is preserved under arbitrary inputs. This lets high-level policies efficiently steer exploration while leaving precise motion to the autonomous behavior. We validate SafePBDS in simulation and on a 23-DOF Franka Panda-Allegro Hand platform. On dexterous grasping, SafePBDS achieves a $92.5\%$ success rate across 20 household objects and 120 trials. Using the action interface, the method can exclude any one of the four fingers during grasping via a one-dimensional action, achieving $94.4\%$ 3-finger grasp success across 3 objects and 36 trials. The efficient planning and safety guarantee of SafePBDS also enables the first model-based, fully actuated palm-down in-hand reorientation, exceeding $360^\circ$ of yaw rotation in both directions under varying object weight and wrist motion. Demo video and details: https://tml.stanford.edu/safe-pbds
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