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 more common in various real-world applications, such as manufacturing and warehouse logistics. However, task allocation and scheduling for a multi-agent team face complex challenges due to the need to simultaneously consider time-extended tasks, task constraints, and uncertainties in execution. Potential task failures or contingencies can add additional tasks to recover from the failures, and reactively addressing contingencies can decrease teaming efficiency. To efficiently and proactively consider contingencies, this paper proposes treating the problem as a multi-robot task allocation under uncertainty problem. We suggest a hierarchical approach that divides the problem into two layers. We use mathematical program formulation for the lower layer to find the optimal solution for a deterministic multi-robot task allocation problem with known task outcomes. The higher-layer search intelligently generates more likely combinations of contingency scenarios and calls the inner-level search repeatedly to find the optimal task allocation sequence for the given scenario. We validate our results in simulation for manufacturing applications and demonstrate that our method can reduce the effect of potential delays from contingencies.Note to Practitioners—Automation engineers interested in deploying robotic cells in low-volume applications need to consider contingency handling. When the occurrence of contingencies can be characterized as probability distributions, it is often useful to consider using a proactive approach for task allocation and scheduling. To implement our algorithm, automation engineers will need to develop a hierarchical task network specified by domain experts that models task constraints and a task-agent duration model, which may be generated from simulation environments. Furthermore, they must identify tasks that can result in contingencies and describe them with a probabilistic model. This model can be generated from historical data and/or real-world experiments. Lastly, for addressing the contingency, the practitioner will need to specify a task procedure to recover from a specific contingency type. To run the algorithm, we found that repeatedly approximating the best proactive task allocation for a fixed computation budget and dispatching the best tasks worked well. The computation budget required to approximate the best task allocation is directly affected by the number of contingency scenarios that can be sampled. Therefore, the practitioner must determine a suitable computational budget empirically based on the number of contingencies that can occur.

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.

We focus on enabling robots to proactively assist humans in assembly tasks by adapting to their preferred sequence of actions. Much work on robot adaptation requires human demonstrations of the task. However, human demonstrations of real-world assemblies can be tedious and time-consuming. Thus, we propose learning human preferences from demonstrations in a shorter, canonical task to predict user actions in the actual assembly task. The proposed system uses the preference model learned from the canonical task as a prior and updates the model through interaction when predictions are inaccurate. We evaluate the proposed system in simulated assembly tasks and in a real-world human-robot assembly study and we show that both transferring the preference model from the canonical task, as well as updating the model online, contribute to improved accuracy in human action prediction. This enables the robot to proactively assist users, significantly reduce their idle time, and improve their experience working with the robot, compared to a reactive robot.

We consider the problem of task assignment and scheduling for human-robot teams to enable the efficient completion of complex problems, such as satellite assembly. In high-mix, low volume settings, we must enable the human-robot team to handle uncertainty due to changing task requirements, potential failures, and delays to maintain task completion efficiency. We make two contributions: (1) we account for the complex interaction of uncertainty that stems from the tasks and the agents using a multi-agent concurrent MDP framework, and (2) we use Mixed Integer Linear Programs and contingency sampling to approximate action values for task assignment. Our results show that our online algorithm is computationally efficient while making optimal task assignments compared to a value iteration baseline. We evaluate our method on a 24-task representative assembly and a real-world 60-task satellite assembly, and we show that we can find an assignment that results in a near-optimal makespan.

Managing robot workloads in human robot teams is critical for efficient team operation. If robots are overloaded with work, then they will miss deadlines and force humans to take on extra work. This paper presents a framework for a robot to assess its own workload based on an initial goal assignment. The robot does this by generating task and motion plans and computing the probability of missing deadlines due to the possibility of delays in task execution. A branch and bound based search is used to generate task and motion plans by minimizing task execution effort. The robot presents a diverse set of task and motion plans to the humans to offer multiple different options. Humans can either approve a plan or provide guidance to reduce the workload by either relaxing deadlines or removing goal(s) assigned to the robots.