具有操纵行为的常时间运动规划
Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors
AI总结：
本文提出B-CTMP算法，通过预计算紧凑数据结构，在固定时间内完成两步操纵任务，确保在半结构化环境中实现安全高效的操纵。
摘要：
近年来，接触丰富的机器人操纵取得了显著进展，但大多数部署系统仍局限于简单的预设程序。关键障碍在于缺乏能够提供安全、高效和可靠保证的运动规划算法。为此，引入了常时间运动规划（CTMP）算法，利用预处理阶段实现碰撞自由的运动查询，在固定用户指定的时间预算内（如10毫秒）。然而，现有CTMP方法未显式整合物体处理所需的关键操纵行为。为此，我们引入了行为常时间运动规划器（B-CTMP），扩展CTMP以解决广泛的两步操纵任务：（1）碰撞自由运动到行为启动状态，随后（2）执行操纵行为（如抓取或插入）以达到目标。通过预计算紧凑数据结构，B-CTMP在毫秒级时间内保证查询，同时确保在指定状态集上完整性和任务成功执行。我们在模拟和真实机器人上评估了B-CTMP在两个典型操纵任务（书架取物和插头插入）中的性能。结果表明，B-CTMP在单一常时间框架内统一了碰撞自由规划和物体操纵，为半结构化环境中的操纵提供了可证明的速度和成功保证。
英文摘要：
Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the barriers is the lack of motion planning algorithms that can provide verifiable guarantees for safety, efficiency and reliability. Constant-Time Motion Planning (CTMP) is a recent step toward such guarantees for collision-free motion in a priori known environments:: a preprocessing phase enables queries to be answered within a fixed, user-specified time budget (e.g., 10 milliseconds). However, CTMP certifies only reachability---a binary predicate---and ignores the manipulation behavior that completes the task, which is increasingly stochastic (e.g., a learned skill) and whose success no single offline rollout can establish, let alone certify. We introduce the Behavioral Constant-Time Motion Planner (B-CTMP), which extends CTMP to two-step manipulation tasks in semi-structured environments: a collision-free motion to a behavior initiation state, followed by execution of a behavior such as grasping or insertion. B-CTMP departs from prior CTMP in two ways: neighborhoods are constructed in object-pose space rather than robot configuration space, and coverage is established by statistical certification rather than a reachability check. A plan is cached only if repeated rollouts lower-bound its success rate above a user-specified threshold, and we prove these bounds hold simultaneously across the entire cache at a prescribed confidence level. For deterministic behaviors a single rollout suffices, recovering the binary check of prior CTMP as a special case. We evaluate B-CTMP on three manipulation tasks---shelf picking, plug insertion, and wheel replacement---in simulation and on real robots. B-CTMP's certified plans succeed consistently where baselines fail during behavior execution, and it rejects infeasible object poses in constant time.
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。
