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.

翻译：现实世界的机器人操作任务通常涉及与环境的力交互，例如使用不同重量的工具、运输不同质量的物体，以及执行桌布擦拭等接触密集型任务。以往的基于学习方法通常采用模仿学习策略，输出由低层阻抗控制器跟踪的目标末端执行器位姿。在这些系统中，力交互要么通过稳态跟踪误差隐式实现，要么通过腕部力/力矩或触觉传感器显式指令实现。然而，隐式方法在物体重量变化时泛化能力较差，而显式方法需要专用硬件并增加系统复杂性。本研究提出IMPACT框架，将这类力控任务解耦为任务规划与基于内模的预测控制。大量仿真和真实世界实验表明，该框架在成功率、对未见物体重量的泛化能力以及安全性和能效方面均实现更优表现。