Published in Preprint
The diversity of touch sensor designs complicates general-purpose tactile processing. We address this by training a diffusion model for cross-modal prediction, translating tactile signals between GelSlim and Soft Bubble sensors. This enables sensor-specific methods to be applied across sensor types.
Published in International Conference on Robotics and Automation (ICRA)
We present a contrastive self-supervised learning method to unify tactile feedback across different sensors, using paired tactile data. By treating paired signals as positives and unpaired ones as negatives, our approach learns a sensor-agnostic latent representation, capturing shared information without relying on reconstruction or task-specific supervision.
Published in 9th Conference on Robotic Learning (CoRL)
We prenset a method for tansferring manipulation policies between different tactile sensors by generating cross-sensor tactile signals. Using either a paired diffusion model (T2T) or an unpaired depth-based approach (T2D2), the method enables zero-shot policy transfer without retraining. We demonstrate it on a marble rolling task, where policies learned with one sensor are successfully applied to another.
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Graduate Course, University of Michigan, Robotics, Fall 2020 & Fall 2021
This course is an introduction to the field of manipulation. The course covers the fundamentals of manipulation, including kinematics, dynamics, control, and planning. The course also covers the fundamentals of grasping and manipulation, including grasp planning, grasp stability, and manipulation planning.
Graduate course, University of Michigan, Robotics, Winter 2023
An introduction to modern machine learning methods for control and planning in robotics. Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning. Students implement the above learning algorithms on robots in simulation.