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Learning Control: Machine learning (ML) is one of the key ingredients on the road to intelligent and autonomous systems However, machine learning for moving systems has received rather little attention, in contrast to the current main stream ML. We are interested in all branches of machine learning that can lead to black-box autonomous learning systems, including reinforcement learning, adaptive control, function approximation, active learning, and many other topics. Read more ...

Another topic: This is the short description of the topic. Read more ...

Active perception:In the Autonomous Motion Department, we see perceptuo-motor skills as units in which motor control and percep- tual processing are informing each other in a tight loop. Particular motor skills might require specific perceptual processing to be successful and vice versa. For example, to visually servo the robot hand to a specific pose rela- tive to an objects requires robust and fast simultaneous tracking of hand and object. Read more ...

Generation and Control of Movements: The research agenda of this group is focused on understanding the fundamental principles underlying the generation of movements in autonomous robots and especially in robots with hands and legs. We are interested in developing theories and algorithms that can be used to create machines that move and interact with their environment in a non-trivial manner. Read more ...

Neural Control of Movement:Given that humans are the living example that robust and intelligent preceptuo-motor behaviors can be realized at an amazing level of performance, it has always been a most interesting research topic to investigate principles of perceptuo-motor control in human an non-human primates. The Autonomous Motion Department is in the process of setting up a research group that will focus on examining neural and behavioral principles of the control of movement. Read more ...

Experimental Robotics:The Autonomous Motion Department will emphasize experiments with actual systems, in particular anthropo- mophic and humanoid robots. The synthesis of autonomous systems with robot requires addressing the real physics of the system, complex and unknown nonlinearities, and it is not possible to bypass some issues as often done in simulation studies. In the past, we realized that investigations by synthesis add enormous value to the research program, as they create novel questions and focus research attention to what is actually relevant. We are currently in the process of developing several experimental platforms. Read more ...
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