Shared Autonomy with Learned Latent Actions (2005.03210v2)

Abstract: Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grained manipulation. For example, when eating, not only does one need to move to the correct food item, but they must also precisely manipulate the food in different ways (e.g., cutting, stabbing, scooping). Shared autonomy methods make robot teleoperation safer and more precise by arbitrating user inputs with robot controls. However, these works have focused mainly on the high-level task of reaching a goal from a discrete set, while largely ignoring manipulation of objects at that goal. Meanwhile, dimensionality reduction techniques for teleoperation map useful high-dimensional robot actions into an intuitive low-dimensional controller, but it is unclear if these methods can achieve the requisite precision for tasks like eating. Our insight is that---by combining intuitive embeddings from learned latent actions with robotic assistance from shared autonomy---we can enable precise assistive manipulation. In this work, we adopt learned latent actions for shared autonomy by proposing a new model structure that changes the meaning of the human's input based on the robot's confidence of the goal. We show convergence bounds on the robot's distance to the most likely goal, and develop a training procedure to learn a controller that is able to move between goals even in the presence of shared autonomy. We evaluate our method in simulations and an eating user study. See videos of our experiments here: https://youtu.be/7BouKojzVyk

Insights Into Shared Autonomy with Learned Latent Actions

The paper "Shared Autonomy with Learned Latent Actions" introduces a novel framework aimed at enhancing the teleoperation of assistive robots, particularly for individuals with physical disabilities who perform tasks requiring both coarse movements and fine motor skills. In the field of assistive robotics, specifically for eating tasks, precise manipulation is required not just for reaching food items but for executing dexterous tasks such as cutting and scooping—a complexity not yet efficiently handled by existing shared autonomy or teleoperation strategies alone.

Key Contributions

The authors propose a system that leverages learned latent actions combined with shared autonomy, propelling the paper of teleoperation in assistive settings. They begin by addressing the limitations of previous work, which typically segment tasks into high-level goals and low-level manipulations but often falter when integrated into a cohesive user experience. The proposed solution integrates these areas, enabling seamless transitions from high-level task navigation to precise manipulation through intuitive control mechanisms.

1. Model Structure and Training: By adopting a novel model structure that adjusts the influence of human input based on the robot's confidence in its goal direction, this research provides a significant contribution to shared autonomy. Importantly, the model includes convergence guarantees, ensuring that robots consistently pursue the most likely user-intended goals while allowing for adjustments when the user's preferences change mid-operation.
2. Simulation and Evaluation: The paper is robustly verified through simulations and user studies. These experiments confirm that integrating shared autonomy with learned latent actions effectively diminishes both task-completion times and idle times, thereby enhancing overall task execution efficiency. In particular, participants using the new LA+SA system could successfully navigate complex, multistep eating tasks and change preferences spontaneously, emphasizing its practical applicability.
3. Entropy and Versatility in Latent Spaces: Incorporating an innovative entropy reward within the learning framework, the authors ensure that users can dynamically adjust their goals even when initial beliefs may have constrained the robot's assistance excessively. This addition is crucial when considering scenarios that involve nuanced user input, further validating the system's applicability across a range of user competencies and scenarios.

Implications and Future Directions

The implications of this work are notable both in the development of assistive technologies and broader applications. Integrating such systems within the teleoperation of robotic arms mounted on wheelchairs could significantly minimize reliance on caregivers, enhancing independence and quality of life for those with physical disabilities.

From a theoretical standpoint, this research enriches the conversation about balancing between autonomy and human input, a dynamic central to safe and effective human-robot interaction.

Looking forward—spanning healthcare to industrial applications—the exploration into embedding latent representations conditioned on evolving operational contexts broadens the horizon for additively manufactured personal robotics and adaptable manufacturing systems. This integration could spur future advancements in adaptable robotic applications beyond assistive contexts, potentially sparking innovations across various domains requiring similar precision and adaptability.

Conclusion

The paper presents a compelling and meticulously evaluated approach to combining the benefits of shared autonomy and learned latent actions. By demonstrating how intuitive embeddings enhance the precision and efficacy of assistive teleoperation, it sets a benchmark for future research aiming to further the integration of autonomous systems in daily human activities.