Disassembly is a critical challenge in maintenance and service tasks, particularly in high-precision operations such as electric vehicle (EV) battery recycling. Tasks like prying-open sealed battery covers require precise manipulation and controlled force application. In our approach we collect human demonstrations using a motion capture system, enabling the robot to learn from human-expert disassembly strategies. These demonstrations train a bimanual robotic system in which one arm exerts force with a specialized tool while the other manipulates and removes sealed components. Our method builds on a diffusion-based policy and integrates real-time force sensing to adapt its actions as contact conditions change. We decompose the demonstrations into distinct sub-tasks and apply data augmentation, thereby reducing the number of demonstrations needed and mitigating potential task failures. Our results show that the proposed method, even with a small dataset, achieves a high task success rate and efficiency compared to a standard diffusion technique. We demonstrate in a real-world application that the bimanual system effectively executes chiseling and peeling actions to separate bonded sheet from a substrate.

Suction cups are popular for picking and transporting packages in warehouse applications. To maximize throughput, high transport speeds are desired. Many packages are deformable and may detach from the suction cups due to inertial loading if trajectories use excessive velocities. This paper introduces a novel methodology that analyzes package deformation through its curvature at the package-suction cup contact interface to generate a Factor-of-Safety (FOS) score for each waypoint in a given trajectory. By maintaining the FOS above a predetermined threshold, the trajectory planner is able to generate transport trajectories that are both safe and time-optimized. Experimental results show the method’s efficacy, demonstrating a 21.92% reduction in transport times compared to a conservative trajectory generation. Our FOS predictor identified trajectories that ensured safe package transport with 100% accuracy across all 627 real-world experiments.