用GPU并行加速机器人任务与运动规划，秒级解决复杂约束问题。
Differentiable GPU-Parallelized Task and Motion Planning
- 基于双层优化框架，利用GPU同时采样数千个初始解
- 通过可微优化在数秒内满足非凸约束并最小化成本
- 适用于真实机器人，显著优于传统串行方法
长时序机器人操作规划需同时做出关于与哪些物体交互的离散决策和如何交互的连续决策。机器人规划器必须选择可行且安全的抓取、放置及运动轨迹。此类问题属于任务与运动规划（TAMP），在算法运行时间和解的质量上面临巨大挑战，尤其当解空间高度受限时。为此，我们提出一种新的双层TAMP算法，利用GPU并行性同时高效探索数千个候选连续解。该方法通过GPU并行采样计划骨架的初始解集，并对这批解进行可微优化，以满足计划约束并最小化相对于软目标的成本。我们证明该算法可在数秒内有效解决具有非凸约束的高约束问题，显著优于串行TAMP方法，并在多个真实机器人上验证了其有效性。
原文摘要 · Abstract (English)
Planning long-horizon robot manipulation requires making discrete decisions about which objects to interact with and continuous decisions about how to interact with them. A robot planner must select grasps, placements, and motions that are feasible and safe. This class of problems falls under Task and Motion Planning (TAMP) and poses significant computational challenges in terms of algorithm runtime and solution quality, particularly when the solution space is highly constrained. To address these challenges, we propose a new bilevel TAMP algorithm that leverages GPU parallelism to efficiently explore thousands of candidate continuous solutions simultaneously. Our approach uses GPU parallelism to sample an initial batch of solution seeds for a plan skeleton and to apply differentiable optimization on this batch to satisfy plan constraints and minimize solution cost with respect to soft objectives. We demonstrate that our algorithm can effectively solve highly constrained problems with non-convex constraints in just seconds, substantially outperforming serial TAMP approaches, and validate our approach on multiple real-world robots. Project website and code: https://cutamp.github.io
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