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Motion Generation [1]


How to find motion strategies that are robust in the real world? We combine control and planning under uncertainty to generate motion for mobile manipulators. more to: Motion Generation [2]

Protein Motion [3]


Understanding the function of a protein requires understanding the protein's motions behind that function. We work towards developing a "computational microscope" that simulates these motions computationally efficient and biologically accurate. more to: Protein Motion [4]

Human Grasping [5]


Can we improve robotic grasping by studying how humans manipulate and grasp objects in their environment? By varying subjects' visual and touch capabilities, we characterize their grasp strategies and transfer them to robots. more to: Human Grasping [6]

Heuristics for Ball Catching [7]


Is it better to use heuristics or optimization for control problems such as ball catching? more to: Heuristics for Ball Catching [8]

Representation Learning [9]


Truly versatile robots cannot rely on representations that are specifically hand-crafted for every task. Instead they must be able to learn these representations from experience. more to: Representation Learning [10]

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