通过搜索分配空间和零空间来体现多手操作策略
Embodying Multi-Hand Manipulation Policies by Searching the Assignment and Null Spaces
AI总结：
研究多臂机器人执行操作策略的问题，基于冲突搜索构建框架，通过搜索轨迹分配空间和零空间，统一处理分配与零空间运动，有效解决多手操作策略执行难题，实现多臂机器人协调操作。
AI中文摘要：
学习到的操作策略越来越多地预测抽象“手”的运动，因其依赖易于收集的演示且能在机器人平台间转移而在实践中颇具吸引力。然而，在多臂机器人上执行这些轨迹并非易事。多手策略输出必须分配给物理臂，各臂须实现跟踪规定末端执行器轨迹的构型空间运动，且所有臂须遵守运动学极限并避免碰撞。在缺乏直接解决此问题算法的情况下，从业者通常临时扩展单臂逆运动学（IK）管道，无法保证可行性或安全性。在这项工作中，我们用基于搜索的框架弥合了这一执行差距，该框架理论上完整地将策略生成的多手轨迹应用于物理多臂系统。基于基于冲突的搜索，我们的方法明确地在轨迹到臂的离散分配以及冗余操纵器的连续雅可比零空间上进行搜索，利用冗余在跟踪规定运动时避免臂间碰撞。这种对分配和零空间运动的统一处理产生了一个实际高效的规划器，能在多臂机器人上安全地实现协调的操作策略输出。
英文摘要：
Learned manipulation policies increasingly predict motions for abstract "hands" and are attractive in practice because they rely on easily collected demonstrations and transfer across robot platforms. Executing these trajectories on multi-arm robots, however, is not trivial. Multi-hand policy outputs must be assigned to physical arms, each arm must realize a configuration-space motion that tracks its prescribed end-effector trajectory, and all arms must respect kinematic limits and avoid collisions. In the absence of algorithms that directly address this problem, practitioners typically extend single-arm inverse-kinematics (IK) pipelines in an ad hoc way, with no guarantees of feasibility or safety. In this work, we close this execution gap with a search-based framework that is theoretically complete for grounding policy-generated multi-hand trajectories onto physical multi-arm systems. Building on Conflict-Based Search, our method explicitly searches over both the discrete assignment of trajectories to arms and the continuous Jacobian null spaces of redundant manipulators, using redundancy to avoid inter-arm collisions while tracking the prescribed motions. This unified treatment of assignment and null-space motion yields a practically efficient planner that safely realizes coordinated manipulation-policy outputs on multi-arm robots. See omcbsa.github.io for more.
