Contact force in contact-rich environments is an essential modality for robots to perform general-purpose manipulation tasks, as it provides information to compensate for the deficiencies of visual and proprioceptive data in collision perception, high-precision grasping, and efficient manipulation. In this paper, we propose an admittance visuomotor policy framework for continuous, general-purpose, contact-rich manipulations. During demonstrations, we designed a low-cost, user-friendly teleoperation system with contact interaction, aiming to gather compliant robot demonstrations and accelerate the data collection process. During training and inference, we propose a diffusion-based model to plan action trajectories and desired contact forces from multimodal observation that includes contact force, vision and proprioception. We utilize an admittance controller for compliance action execution. A comparative evaluation with two state-of-the-art methods was conducted on five challenging tasks, each focusing on different action primitives, to demonstrate our framework's generalization capabilities. Results show our framework achieves the highest success rate and exhibits smoother and more efficient contact compared to other methods, the contact force required to complete each tasks was reduced on average by 48.8%, and the success rate was increased on average by 15.3%. Videos are available at https://ryanjiao.github.io/AdmitDiffPolicy/.

翻译：在接触丰富的环境中，接触力是机器人执行通用操作任务的重要模态，因为它提供了补偿视觉和本体感知数据在碰撞感知、高精度抓取和高效操作方面不足的信息。本文提出了一种用于连续、通用、接触丰富操作的导纳视觉运动策略框架。在演示阶段，我们设计了一个低成本、用户友好的带接触交互遥操作系统，旨在收集顺应性机器人演示并加速数据收集过程。在训练和推理阶段，我们提出了一种基于扩散的模型，用于从包含接触力、视觉和本体感知的多模态观测中规划动作轨迹和期望接触力。我们采用导纳控制器执行顺应性动作。通过在五个聚焦不同动作基元的挑战性任务上与两种最先进方法进行比较评估，展示了我们框架的泛化能力。结果表明，与其他方法相比，我们的框架实现了最高的成功率，并展现出更平滑、更高效的接触，完成每项任务所需的接触力平均降低了48.8%，成功率平均提高了15.3%。演示视频可在 https://ryanjiao.github.io/AdmitDiffPolicy/ 查看。