分层控制让机械手实时抓取更安全，无需重训即可动态避障。
Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
- 高层用强化学习生成抓取动作指令，底层用QP优化求解关节速度
- 实测7自由度臂+20自由度手可零样本抓取未见物体并抗干扰
- 操作员可实时调整安全边界，适合复杂动态环境的灵巧操作
本文提出一种分层混合控制框架，实现反应式灵巧抓取。通过将高层空间意图与底层关节执行显式解耦，设计了专门用于机械臂和手部的多智能体强化学习架构，作为高层规划器生成任务空间速度指令。这些指令由支持GPU并行的二次规划控制器处理，转化为满足运动学约束与碰撞规避的可行关节速度。该结构不仅加速训练收敛，还严格保障硬件安全。此外，系统具备零样本可调节性，操作员可动态调整安全裕度、避开动态障碍物而无需重新训练策略。通过严格的仿真到现实迁移流程验证，7自由度机械臂搭配20自由度仿人手在真实硬件上成功实现对多种未见物体的鲁棒零样本抓取，展现了在非结构化环境中对意外物理扰动的实时恢复能力。
原文摘要 · Abstract (English)
In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent from low-level joint execution. We introduce a multi-agent reinforcement learning architecture, specialized into distinct arm and hand agents, that acts as a high-level planner by generating desired task-space velocity commands. These commands are then processed by a GPU-parallelized quadratic programming controller, which translates them into feasible joint velocities while strictly enforcing kinematic limits and collision avoidance. This structural isolation not only accelerates training convergence but also strictly enforces hardware safety. Furthermore, the architecture unlocks zero-shot steerability, allowing system operators to dynamically adjust safety margins and avoid dynamic obstacles without retraining the policy. We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline. Real-world hardware experiments on a 7-DoF arm equipped with a 20-DoF anthropomorphic hand demonstrate highly robust zero-shot transferability for dexterous grasping to a diverse set of unseen objects, highlighting the system's ability to reactively recover from unexpected physical disturbances in unstructured environments.
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