Shared autonomy (SA), which combines user inputs with autonomous capabilities, presents a significant opportunity for assistive robotics. A key challenge in SA is the dimensionality gap: the mismatch between low-dimensional user inputs from familiar interfaces (e.g., 2D joysticks) and the high-dimensional control required by robot manipulators. To enhance usability and acceptance, this mapping must be as simple and intuitive as possible. We introduce GeoSACS, a geometric framework for SA. GeoSACS uses canal surfaces to encode task structure with as few as two demonstrations. While the robot moves autonomously along the canal, users can then make corrections on the 2D planar circular cross-sections orthogonal to the robot motion. By leveraging geometric structure to partition the 6D control space between the robot and the user, GeoSACS allows the intuitive mapping of 2D user inputs to 6D end-effector control. We describe GeoSACS and evaluate its underlying assumptions in a user study against two baselines. Results from the study demonstrate reduced workload and improved performance, providing insights for the design of future SA systems.

The deployment of assistive robotics technologies in human environments is often hindered by the diverse personalization requirements of individual users. A promising solution to this challenge is shared autonomy, enabling collaboration between robots and users. The goal of my PhD is to develop shared autonomy methods that empower users with disabilities to control assistive robots efficiently in their daily lives. Specifically, I aim to address the dimensionality gap encountered when using simpler interfaces while maintaining an intuitive input mapping. Our prior work introduced a geometric shared autonomy approach based on canal surfaces and a dynamic input mapping framework, that serve as foundational efforts in this area. To gather preliminary feedback, I conducted a user study involving wheelchair users, utilizing a specialized joystick designed for accessible video gaming. In future work, I plan to incorporate computer vision to eliminate reliance on demonstrations and use participatory design to enhance our interface design.

While shared autonomy offers significant potential for assistive robotics, key questions remain about how to effectively map 2D control inputs to 6D robot motions. An intuitive framework should allow users to input commands effortlessly, with the robot responding as expected, without users needing to anticipate the impact of their inputs. In this article, we propose a dynamic input mapping framework that links joystick movements to motions on control frames defined along a trajectory encoded with canal surfaces. We evaluate our method in a user study with 20 participants, demonstrating that our input mapping framework reduces the workload and improves usability compared to a baseline mapping with similar motion encoding. To prepare for deployment in assistive scenarios, we built on the development from the accessible gaming community to select an accessible control interface. We then tested the system in an exploratory study, where three wheelchair users controlled the robot for both daily living activities and a creative painting task, demonstrating its feasibility for users closer to our target population.