Progress in symmetry preserving robot perception and control through geometry and learning.

This article reports on recent progress in robot perception and control methods developed by taking the symmetry of the problem into account. Inspired by existing mathematical tools for studying the symmetry structures of geometric spaces, geometric sensor registration, state estimator, and control methods provide indispensable insights into the problem formulations and generalization of robotics algorithms to challenging unknown environments. When combined with computational methods for learning hard-to-measure quantities, symmetry-preserving methods unleash tremendous performance. The article supports this claim by showcasing experimental results of robot perception, state estimation, and control in real-world scenarios.

Integration of artificial vision with non-visual peripheral cues to guide mobility.

Visual prostheses can improve vision for people with severe vision loss, but low image resolution and lack of peripheral vision limit their effectiveness. To address both problems, we developed a prototype advanced video processing system with a headworn depth camera and feature detection capabilities. We used computer vision algorithms to detect landmarks representing a goal and plan a path towards the goal, while removing unnecessary distractors from the video. If the landmark fell outside the visual prosthesis's field-of-view (20 degrees central vision) but within the camera's field-of-view (70 degrees), we provided vibrational cues to the left or right temple to guide the user in pointing the camera. We evaluated an Argus II retinal prosthesis participant with significant vision loss who could not complete the task (finding a door in a large room) with either his remaining vision or his retinal prosthesis. His success rate improved to 57%, 37.5%, and 100% while requiring 52.3, 83.0, and 58.8 seconds to reach the door using only vibration feedback, retinal prosthesis with modified video, and retinal prosthesis with modified video and vibration feedback, respectively. This case study demonstrates a possible means of augmenting artificial vision. Clinical Relevance- Retinal prostheses can be enhanced by adding computer vision and non-visual cues.