Bashar Kabbarah

Bashar Kabbarah

Biography

Bashar Kabbarah is a rising sophomore studying Electrical Engineering and Computer Sciences at UC Berkeley, where he is a Regents’ Scholar and a member of the SEED Scholars Honors Program. His first research project, medical imaging for spinal disease, later published in the Journal of Emerging Investigators, exposed him to the proclivity of high-stakes machine learning models to fail to generalize beyond lab conditions. As an MIT Media Lab intern in Professor Paul Liang’s Multisensory Intelligence group, he now addresses this limitation by leveraging multimodality, researching AI systems that combine vision, language, touch, and even smell to provide richer sensory grounding that improves model robustness. His past experience spans Lawrence Berkeley National Laboratory, UC Berkeley’s DNA Sequencing Facility, and industry work at BFAI Semiconductor Solutions, where he improved computer vision models for defect detection. Bashar intends to pursue a PhD in multimodal representation learning for safety-critical domains like medicine and manufacturing.

Piezoresistive Palm Reading: Multimodal Alignment and Prediction from Touch
for Dexterous Manipulation
Bashar Kabbarah¹, Kaichen Zhou², Yuxin Ray Song² and Paul Pu Liang^2
1Electrical Engineering and Computer Sciences, University of California, Berkeley
²Media Lab, Massachusetts Institute of Technology

A wide range of sensory signals help dexterous robots manipulate objects reliably. First person video captures the appearance of objects and interaction but not contact, force, or grip, leaving gaps that tactile information can fill. The OpenTouch framework aligns touch, egocentric video, and hand pose in a shared representation using contrastive learning. We first improve alignment, then ask whether touch predicts how a hand will move next. Replacing average pooling with a temporal pose encoder improves tactile-to-pose retrieval 2.7x, from 16.8 to 45.5 mAP. To test prediction, we isolate finger articulation from whole-hand motion and decode its future direction from touch available only up to the present moment. Touch predicts that direction well above chance, at AUC 0.60 to 0.69, while shuffled-touch controls remain at AUC 0.50. Adding touch to a pose encoder with the same temporal history still improves prediction, across horizons from 67 to 533 milliseconds, and helps most when predicting whether the fingers are about to curl. Touch therefore supplies information that hand kinematics alone do not carry, revealing not just what a hand is holding but how it is about to reshape. This suggests robots could use contact to anticipate hand motion rather than react to it.