Master of Science in Robotics Thesis Talk
Speaker
XINJIE YAO
Masters Student
Robotics Institute
Carnegie Mellon University
When
-
Where
In Person and Virtual - ET
Description
Under shared autonomy, wheelchair users expect vehicles to provide safe and comfortable rides while following users’ high-level navigation plans. To find such a path, vehicles negotiate with different terrains and assess their traversal difficulty. Most prior works model surroundings either through geometric representations or semantic classifications, which do not reflect perceived motion intensity and ride comfort in downstream navigation tasks. We propose to model ride comfort explicitly in traversability analysis using proprioceptive sensing. We develop a self-supervised learning framework to predict traversability costmap from first-person-view images by leveraging vehicle states as training signals. Our approach estimates how the vehicle would “feel” if traversing over based on terrain appearances. We then show our navigation system provides human-preferred ride comfort through robot experiments together with a human evaluation study.
Committee: Prof. Jean Oh Prof. Ji Zhang Prof. Stephen Smith Tanmay Shankar
In Person and Zoom Participation. See announcement.