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Inhalt des Dokuments

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. mehr zu: 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. mehr zu: 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. mehr zu: Human Grasping [6]

Heuristics for Ball Catching [7]


Is it better to use heuristics or optimization for control problems such as ball catching? mehr zu: 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. mehr zu: Representation Learning [10]

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