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Interactive Perception [1]

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Action is a crucial part of the perceptual process. Humans interact with their environment constantly to reveal information and use the interaction to understand the consequences of their actions in the world. Interactive Perception exploits these ideas to build autonomous mobile manipulators that use interactions to perceive, understand and manipulate their environment. more to: Interactive Perception [2]

Representation Learning [3]

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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 [4]

Manipulation Planning [5]

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Brushing teeth, eating, opening a door: humans interact with the environment countless times each day without thinking of it. Manipulation planning requires the understanding of Object, Robot, and Environment (ORE) interactions. We research the "ORE triangle" to advance the field and shift the manipulation planning paradigm toward new and exciting realms. more to: Manipulation Planning [6]

Soft Hands [7]

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We design, build, and use soft robotic hands, like the RBO Hand 2. Made from rubber and actuated pneumatically, they are highly compliant and extremely robust. With them we can reliably grasp objects with uncertain geometries and positions, by offloading some perceptual difficulties to the morphological computation of the hand itself. more to: Soft Hands [8]

Motion Generation [9]

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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 [10]

Protein Structure Prediction [11]

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What shape does a protein take when it folds? This questions have preoccupied researchers for decades. We are building algorithms that leverage experimental and evolutionary data to answer this question, and predict the structures of proteins from their sequences. more to: Protein Structure Prediction [12]

Protein Motion [13]

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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 [14]

Human Grasping [15]

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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 [16]

Heuristics for Ball Catching [17]

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Is it better to use heuristics or optimization for control problems such as ball catching? more to: Heuristics for Ball Catching [18]

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