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About

Snehal Dikhale is a robotics researcher specializing in visuotactile perception, in-hand object manipulation, and 6D pose estimation — areas critical to advancing dexterous robotic systems. His most influential work, "VisuoTactile 6D Pose Estimation of an In-Hand Object Using Vision and Tactile Sensor Data" (2022, 51 citations), tackled a longstanding challenge in robotics: accurately estimating an object's pose during manipulation, where robot grippers cause heavy visual occlusion that defeats vision-only approaches. By fusing tactile and visual sensor data, Dikhale demonstrated a powerful pathway to overcoming this fundamental limitation. Building on this foundation, he introduced ViHOPE (2023), which further improves pose estimation accuracy through explicit 3D shape completion, and explored proprioceptive-only approaches using hierarchical graph neural networks, broadening the toolkit available to roboticists. His more recent work, HyperTaxel (2024), addresses the low spatial resolution of taxel-based tactile sensors through contrastive learning, pushing toward human-like tactile dexterity in robots. Across his research portfolio, Dikhale has established himself as a thoughtful innovator at the intersection of tactile sensing and robot perception, with his cumulative work garnering over 70 citations and influencing ongoing developments in intelligent robotic manipulation.