08/2023: My work CCRL now published as an article on Robotics and Automation Letters 2023
Research
My current research focus involves creating learning-based policies that enable high-DOF robots to
exhibit natural fluid behaviors, all while reducing the reliance on intricate reward engineering.
To this end, my ongoing work involves using Large Language Models (LLMs) for iterative, human-guided
refinement for learned control policies.
Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively
leveraging a library of previously learned control policies.
We generate realistic hand manipulations of object by using a Generative Adversarial Network by
conditioning on the hand and finger pose. We adopt an encoder-decoder architecture where we input a
rough rendering of the skeletal pose and get a realistic rendering of a hand as the output. We
generate datasets with simulated and real human arms at different poses and use it to train our
model. Given an hand pose and object pose we showcase a pipeline to generate an image conditioned on
the poses.
Design an automated eye-dropper that detects the patients eye, adminsters the eye drop and registers
if the adminstration was successful using CV (eye-tracking, blink detection, drop tracking).