In the present dissertation, we investigate learning of force patterns from single and force control policies from multiple task demonstrations as well as adaptive control approaches for performance of constrained robotic motions in unknown and deformable environments. On one hand, learning by demonstration helps to transfer human-like skills to the robot. On the other hand, the adaptive control laws help to deal with task uncertainty and are suitable for multiple-input multiple-output, linear and nonlinear, systems.
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In the present dissertation, we investigate learning of force patterns from single and force control policies from multiple task demonstrations as well as adaptive control approaches for performance of constrained robotic motions in unknown and deformable environments. On one hand, learning by demonstration helps to transfer human-like skills to the robot. On the other hand, the adaptive control laws help to deal with task uncertainty and are suitable for multiple-input multiple-output, linear a...
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