PRISM实现双臂在复杂环境中的实时协调操作，提升成功率与鲁棒性。
PRISM: Projection-Integrated Sampling-Based MPC with Bayesian Cost Tuning for Bimanual Manipulation
- 用基于QP的采样策略分离轨迹探索与运动可行性，确保双臂动作合理。
- 在PerAct²任务中成功完成搬运、传递、举盒等挑战，成功率优于基线方法。
- 支持真实机器人部署，适合需要高精度双臂协同的工业场景。
在杂乱且接触频繁的环境中进行双臂操作仍具挑战，需协调运动生成、交互感知规划，并在紧约束下可靠执行。本文提出PRISM，一种融合投影的采样型模型预测控制框架，采用GPU加速的物理模拟器作为在线世界模型，用于复杂双臂操作。核心算法创新在于基于QP的控制采样策略，将轨迹探索与运动可行性解耦。每个MPC步骤中，采样的关节速度轨迹先投影至满足位置、速度、加速度和加速度变化率（jerk）边界及初始速度条件的可行集合，再进行滚动评估，从而实现广泛而可行的双臂协同行为探索。为支持高效在线执行，设计了定制化的ADMM/Bregman分裂二次规划求解器，利用关节可分性与可重用矩阵分解。此外，使用贝叶斯优化离线调优任务代价权重，减少人工参数调整。在多种复杂版本的PerAct²任务上进行评估，包括障碍物约束下的球体搬运、托盘运输、立方体交接和盒子抬升。实验表明，相比代表性采样基线方法，PRISM在保持实时或近实时运行的同时，显著提升了鲁棒性和任务成功率。还在双臂UR5e机器人上成功实现仿真到现实的迁移，凸显了基于物理的在线规划在接触丰富双臂操作中的实用潜力。项目详情（含代码与补充视频）见：https://sites.google.com/view/prismbimanual。
原文摘要 · Abstract (English)
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}}.
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