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.

Multi-robot systems are becoming increasingly prevalent in various real-world applications, such as manufacturing and warehouse logistics. These systems face complex challenges in 1) task allocation due to factors like time-extended tasks, and agent specialization, and 2) uncertainties in task execution. Potential task failures can add further contingency tasks to recover from the failure, thereby causing delays. This paper addresses the problem of Multi-Robot Task Allocation under Uncertainty by proposing a hierarchical approach that decouples the problem into two levels. We use a low-level optimization formulation to find the optimal solution for a deterministic multi-robot task allocation problem with known task outcomes. The higher-level search intelligently generates more likely combinations of failures and calls the inner-level search repeatedly to find the optimal task allocation sequence, given the known outcomes. We validate our results in simulation for a manufacturing domain and demonstrate that our method can reduce the effect of potential delays from contingencies. We show that our algorithm is computationally efficient while improving average makespan compared to other baselines.

In manufacturing, minimizing operational delays is crucial for efficiency and resilience. Therefore, efficiently handling contingencies is essential in human-robot teams working on assembly (i.e., collaborative assembly) applications. This paper introduces a novel approach to generating contingency handling procedures by leveraging recent advances in Large Language Models (LLMs). Our approach uses LLMs to update the required tasks in hierarchical task networks (HTNs) to handle contingencies. The results demonstrate that our approach can handle various contingencies in assembly applications and minimize the impact on the assembly completion time.