Contact Compliance Visuo-Proprioceptive Policy for Contact-Rich Manipulation with Cost-Efficient Haptic Hand-Arm Teleoperation System

Learning robot manipulation skills in real-world environments is extremely challenging. Robots learning manipulation skills in real-world environments is extremely challenging. Recent research on imitation learning and visuomotor policies has significantly enhanced the ability of robots to perform manipulation tasks. In this paper, we propose Admit Policy, a visuo-proprioceptive imitation learning framework with force compliance, designed to reduce contact force fluctuations during robot execution of contact-rich manipulation tasks. This framework also includes a hand-arm teleoperation system with vibrotactile feedback for efficient data collection. Our framework utilizes RGB images, robot joint positions, and contact forces as observations and leverages a consistency-constrained teacher-student probabilistic diffusion model to generate future trajectories for end-effector positions and contact forces. An admittance model is then employed to track these trajectories, enabling effective force-position control across various tasks.We validated our framework on five challenging contact-rich manipulation tasks. Among these tasks, while improving success rates, our approach most significantly reduced the mean contact force required to complete the tasks by up to 53.92% and decreased the standard deviation of contact force fluctuations by 76.51% compared to imitation learning algorithms without dynamic contact force prediction and tracking.

Active Admittance Control with Iterative Learning for General-Purpose Contact-Rich Manipulation

Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper proposes a novel hybrid control framework based on active admittance control with iterative learning parameters-tunning mechanism. The method adopts admittance control as the underlying algorithm to ensure flexibility, and iterative learning as the high-level algorithm to regulate the parameters of the admittance model. The whole algorithm has flexibility and learning ability, which is capable of achieving the goal of excellent versatility. Four representative interactive robot manipulation tasks are chosen to investigate the consistency and generalisability of the proposed method. Experiments are designed to verify the effectiveness of the whole framework, and an average of 98.21% and 91.52% improvement of RMSE is obtained relative to the traditional admittance control as well as the model-free adaptive control, respectively.