基于并行采样的模型预测控制在真实机器人上实现接触丰富操作，性能优于传统方法。
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
- 用JAX与高保真模拟器实现大规模并行采样优化
- 在真实机械臂上完成推移任务，结构化采样显著优于基线方法
- 揭示了实时域随机化信号的局限性，适合研究接触敏感控制的团队
基于采样的模型预测控制（SMPC）是一种有前景的接触丰富型机器人操作策略，结合无梯度优化与大规模并行GPU仿真。然而，以往工作多依赖简化动力学或仅限于仿真环境。本文提出一种利用JAX实现大规模并行计算、并与高保真MuJoCo MJX模拟器集成的MPC框架，并通过完整的“真实-仿真-真实”流程，在Franka Research 3机械臂上执行推移（Push-T）任务。采用结构化全局采样的MTP变体在需模式切换的任务中，优于CEM、MPPI和PS等单峰基线方法，且在仿真与硬件上均表现优异。此外，我们评估了在线域随机化在采样预算内的效果，发现接触触发参数能提供可解释的适应信号，而全局物理参数反馈过弱，难以在典型重规划频率下可靠利用。这些结果凸显了接触丰富操作中采样式MPC的关键挑战：接触敏感性、紧凑计算预算，以及实时获取有效域随机化信号的困难。
原文摘要 · Abstract (English)
Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with massively parallel GPU simulation. Yet, most prior work relies on simplified dynamics or remains confined to simulation. We present an MPC framework that leverages JAX for large-scale parallelization and efficient computation, coupled with the high-fidelity MuJoCo MJX simulator, and deploy it on a Franka Research 3 executing the Push-T manipulation task through a complete real-to-sim-to-real pipeline. The MTP variant with structured global sampling outperforms unimodal baselines such as CEM, MPPI, and PS across tasks that require mode switching, both in simulation and on hardware. Furthermore, we evaluate online domain randomization within the MPC sample budget, showing that contact-initiation parameters yield interpretable adaptation signals, whereas global physics parameters provide feedback that is too weak for reliable exploitation at typical replanning frequencies. These findings highlight key challenges for sampling-based MPC in contact-rich manipulation-contact sensitivity, tight compute budgets, and the difficulty of obtaining informative domain-randomization signals in real time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。