Projects

Sim-2-Real Transfer for Robots 

Developed Reinforcement Learning based algorithms that enable learning bipedal and quadrupedal walking controllers.

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Learning Safe-Falling Strategies for Humanoids 

We develop an off-policy reinforcement learning algorithm with a mixture of actor-critic experts to teach robots how to fall by minimizing impulse. 

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Improving Humanoid Safety by Combining Model-Based Control with Reinforcement Learning

We develop an algorithm to teach robots ability to balance without falling when an external push is applied. We combine model-based control inputs with model free policy learning to improve performance. We also present a curriculum to enable efficient learning.

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Joint space control 

We develop a curriculum leaning algorithm to learn joint space control of robot manipulators which can achieve low end-effector error (<1cm) ,generate smooth control comparable to Operational space control and avoid obstacles. 

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Learning To Prevent Falls with Assistive Device

We develop reinforcement learning algorithm to learn fall prevention control policy for an assistive device like lower-limb exoskeleton. 

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Visio-tactile control policy for multi-fingered robot hand

We investigated the effect of incorporating different sensing modalities such as tactile and vision on policy performance on tasks such as grasping on real world Kuka-Allegro robot system.   

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Learning to Walk on Treadmill 

We learn a control policy to create human agents that can walk on treadmill in simulation. The biomechanical gait characteristics of the human agent is similar to real-world human walking

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