Incremental Learning for Robot Shared Autonomy

Abstract: Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and remain static after pretraining, limiting their ability to handle real-world variations. Even with extensive training data, unforeseen challenges--especially those that fundamentally alter task dynamics, such as unexpected obstacles or spatial constraints--can cause assistive policies to break down, leading to ineffective or unreliable assistance. To address this, we propose ILSA, an Incrementally Learned Shared Autonomy framework that continuously refines its assistive policy through user interactions, adapting to real-world challenges beyond the scope of pre-collected data. At the core of ILSA is a structured fine-tuning mechanism that enables continual improvement with each interaction by effectively integrating limited new interaction data while preserving prior knowledge, ensuring a balance between adaptation and generalization. A user study with 20 participants demonstrates ILSA's effectiveness, showing faster task completion and improved user experience compared to static alternatives. Code and videos are available at https://ilsa-robo.github.io/.

• The paper introduces ILSA, a novel framework that reduces reliance on expert demonstrations through synthetic pretraining and incremental fine-tuning.
• The paper demonstrates that ILSA continuously refines its control policies through user interactions, enabling adaptable and personalized assistive robotics.
• The paper validates ILSA's effectiveness with comprehensive ablation and user studies, confirming robust performance in complex, long-horizon tasks.

The paper "Incremental Learning for Robot Shared Autonomy" addresses the challenge of improving the usability and accessibility of assistive robotic arms through shared autonomy. Traditional methods for developing these systems often depend heavily on expert demonstrations, which can be expensive and inflexible when adapting to new environments or tasks post-deployment.

To tackle these limitations, the paper introduces a novel framework called ILSA (Incrementally Learned Shared Autonomy). ILSA is designed to enhance its assistive control policies continually through interactions with users. This approach allows for greater adaptability and personalization, essential for assistive technologies that cater to diverse user needs.

Key features of the ILSA framework include:

1. Synthetic Kinematic Trajectories for Pretraining: By utilizing synthetic data, ILSA reduces the dependency on costly expert demonstrations during its initial training phase. This step helps establish a foundational model that can be incrementally improved.
2. Incremental Finetuning: After each user interaction, ILSA incrementally refines its policy. This continuous learning process enables the framework to adapt and enhance its performance over time, even after deployment.
3. Knowledge Balance Mechanisms: The paper highlights the importance of balancing the acquisition of new knowledge with the retention of existing knowledge during incremental learning. This ensures that the system remains effective across a range of tasks without losing previously acquired skills.

The effectiveness of ILSA is validated through a comprehensive ablation study and a user study involving 20 participants. These studies assess both quantitative performance metrics and qualitative feedback from users, demonstrating the framework’s robustness and effectiveness in handling complex, long-horizon tasks.

Overall, ILSA represents a significant step forward in shared autonomy, providing a more adaptable and user-responsive solution to assistive robotics. The availability of code and supplementary materials at their website suggests an openness to further research and application in the field.