Vision, Touch & Sound for Robustness & Generalizability in Robotic Manipulation
Explore cutting-edge research on robotic manipulation skills in this Stanford University seminar. Delve into the challenges of high-dimensional state and action spaces, as well as sensor and motor control uncertainties. Discover how Assistant Professor Jennifer Bohg investigates representations of raw perceptual data to enhance robot learning and performance, particularly focusing on the integration of touch sensing with other modalities. Learn about crossmodal compensation models, corrupted sensor detection, and the application of deep learning and physically interpretable parameters in robotic manipulation. Examine innovative approaches like differentiable audio rendering and impact sound modeling for improved robot perception and control. Gain insights into end-to-end learning, robot source separation experiments, and key takeaways from the DiffImpact project, all aimed at advancing robustness and generalizability in robotic manipulation.