发表机构
University of Tartu（塔尔图大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PRISM框架，结合QP引导采样策略与定制QP求解器、贝叶斯优化调优成本，在PerAct²任务上提升双臂操作鲁棒性与成功率，实现仿真到真实的迁移。
AI 中文摘要
在杂乱且接触丰富的环境中进行双臂操作仍具有挑战性，因为它需要协调的运动生成、感知交互的规划，以及在严格运动学约束下的可靠执行。本文提出了PRISM，这是一种集成投影的基于采样的模型预测控制（MPC）框架，它采用GPU加速的物理模拟器作为在线世界模型，用于复杂的双臂操作。主要的算法贡献是一种QP引导的控制采样策略，该策略将轨迹探索与运动学可行性解耦。在每个MPC步骤中，采样的关节速度轨迹会被投影到满足关节位置、速度、加速度和加加速度（jerk）边界以及初始速度边界条件的运动集合上，然后再进行展开评估，这使得能够对协调的双臂行为进行广泛且可行的探索。为支持高效的在线执行，本文推导了一种定制的ADMM/Bregman分裂QP求解器，该求解器利用关节方向的可分性和可重用的矩阵分解。本文还使用贝叶斯优化离线调优任务成本权重，减少手动参数选择。本文在PerAct²任务的具有挑战性变体上评估了PRISM，包括障碍物约束下的球运输、托盘运输、立方体交接和箱子提升。实验表明，与代表性的基于采样的基线相比，PRISM的鲁棒性和任务成功率得到提升，同时保持实时或接近实时的执行。本文还在双UR5e机械臂上展示了成功的仿真到真实的迁移，凸显了基于物理的在线规划在接触丰富的双臂操作中的实际潜力。项目详情（包括代码和补充视频）可在[this https URL]获取。
英文摘要
Bimanual manipulation in cluttered, contact-rich environments remains challenging because it requires coordinated motion generation, interaction-aware planning, and reliable execution under tight kinematic constraints. We present PRISM, a projection-integrated sampling-based Model Predictive Control (MPC) framework that uses a GPU-accelerated physics simulator as an online world model for complex dual-arm manipulation. The main algorithmic contribution is a QP-guided control sampling strategy that decouples trajectory exploration from kinematic feasibility. At each MPC step, sampled joint-velocity trajectories are projected onto the set of motions satisfying joint position, velocity, acceleration, and jerk bounds, together with an initial-velocity boundary condition, before rollout evaluation. This enables broad yet feasible exploration of coordinated bimanual behaviors. To support efficient online execution, we derive a custom ADMM/Bregman-splitting QP solver that exploits joint-wise separability and reusable matrix factorizations. We further use Bayesian optimization to tune task-cost weights offline, reducing manual parameter selection. We evaluate PRISM on challenging variants of PerAct$^{2}$ tasks, including obstacle-constrained ball transport, tray transport, cube handover, and box lifting. Experiments show improved robustness and task success relative to representative sampling-based baselines, while maintaining real-time or near-real-time execution. We also demonstrate successful sim-to-real transfer on dual UR5e manipulators, highlighting the practical potential of physics-based online planning for contact-rich bimanual manipulation. Project details, including code and supplementary videos, are available at \href{https://sites.google.com/view/prismbimanual}{\texttt{https://sites.google.com/view/prismbimanual}}.
