Shared autonomy can improve teleoperating robotic systems in complex manufacturing and assembly tasks by combining human decision-making and robotic capabilities. A key aspect of seamless collaboration and trust in shared autonomy is the robot’s ability to interpret human intentions in a consistent and explainable manner. To achieve this, a graph neural network-based intention estimation framework is introduced, which generates dynamic graphs that capture spatial relationships evolving over time. The framework predicts human intentions at two hierarchical levels: low-level actions and high-level tasks. Furthermore, we empirically and anecdo-tally verify the correctness and consistency of the predictions using explainability metrics. The algorithm is demonstrated by teleoperating a bi-manual robot to assemble various block structures in a virtual reality simulation environment.

We address the problem of intention estimation in human-robot teleoperation, which involves identifying the task being completed and predicting the next actions. Our approach sequentially quantifies the similarity between the observed action sequence and nominal action sequences representing possible tasks using the edit distance metric. Task estimation and action prediction are then performed using a nearest-neighbor rule. A key advantage of our approach is its robustness to deviations in operator actions and action recognition errors, commonly encountered in real-world teleoperation settings. Through extensive experiments on both real and simulated data, we demonstrate that our method largely outperforms alternative approaches, including probabilistic graphical models and transformer-based methods, particularly in scenarios with significant action deviations or action recognition errors. Additionally, we construct task distance matrices to analyze task similarities and potential confusion points, providing insights into when and where estimation errors are likely to occur. This analysis can guide the design of more distinctive task sequences and further improve the reliability of teleoperated robotic systems.

We propose a new approach to solving the problem of intention estimation in human-robot teleoperation for assembly tasks, which includes task estimation and action prediction. Our approach uses probabilistic graphical models to represent the joint distribution of the task and the actions to be taken to complete the task. Both model learning and inference are implemented with Pyro, a state-of-the-art probabilistic programming language. The distinctive feature from the traditional hidden Markov model type of probabilistic methods is that our model takes the time information into account and explicitly models the individual distributions of all the variables under consideration. By doing this, we fully utilize the power of probabilistic programming, and achieve accurate distribution hence uncertainty estimations. Working with a pretrained action recognition module, the proposed model can be trained solely on a tiny instruction manual of the assembly tasks and can be retrained with minimal overhead whenever the manual is changed or augmented, avoiding the need for the costly data reannotation and retraining by the end-to-end learning based methods. We also compare our method with a transformer based model trained directly on the instruction manual, and our method shows superior accuracy in both intention estimation and their distribution estimations. We additionally identify failure cases of both our method and the transformer-based method, and envision methods for improvement.

Quantifying transparency requires evaluating the transparency embedded in the various system design elements to determine how they impact one another and influence human-collective interactions. Prior work demonstrated limitations of an abstract collective interaction. Interface designs to address these limitations and improve human-collective interaction transparency were evaluated for a sequential best-ofN decision-making task with four collectives, each consisting of 200 individual entities. The Informed and Simple visualizations’ predictive progress bars improved transparency and the overall human-collective team performance.