让机器人更稳地用力抓物，还能自适应不同重量的物体。
IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation
- 分离任务规划与内模预测控制，提升力控精度
- 真实世界测试中成功率更高，对未知重量物体泛化更强
- 无需额外传感器，适合工业级机械臂部署
现实中的机器人操作常涉及与环境的大力交互，如使用不同重量的工具、搬运质量各异的物体，以及执行如擦桌子等高接触任务。以往基于学习的方法通常采用模仿学习策略，输出目标末端位姿，由低层阻抗控制器跟踪。此类系统中，力控要么通过稳态跟踪误差隐式实现，要么依赖腕部力/力矩或触觉传感器显式指令。然而，隐式方法在不同物体重量下泛化能力差，显式方法则需专用硬件并增加系统复杂性。本文提出IMPACT框架，将力控任务解耦为任务规划与基于内模的预测控制。大量仿真与真实实验表明，该框架在未见过物体重量下仍保持更高成功率，且安全性与能效表现更优。
原文摘要 · Abstract (English)
Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In these systems, forceful interactions are either implicitly realized through steady-state tracking errors or explicitly commanded using wrist force/torque or tactile sensors. However, implicit approaches generalize poorly across object weights, while explicit approaches require specialized hardware and increase system complexity. In this work, we propose IMPACT, a framework that decouples these forceful tasks into task-planning and internal-model-based predictive control. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves higher success rates and improved generalization to unseen object weights, as well as better safety and energy efficiency.
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