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Projected inverse dynamics control for wiping a board with a single Kuka arm

UniBi/TUBS and BRHM started to build the first components of a so-called Gazebo-Orocos simulation framework by re-implementing the projected inverse dynamics control approach for wiping a board with a single Kuka robot arm based on a recent paper by BRHM.

Paper link: http://ieeexplore.ieee.org...

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Multiple task optimization with a mixture of controllers for motion generation

This video shows the simulated motions for the virtual 3 DOF pendulum and the humanoid robot COMAN. The initial mixture coefficients are compared to optimized coefficients for new reaching targets that were not part of the optimization process.

Paper link: http://ieeexplore.ieee.org...

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Continuous Task-Priority Rearrangement during Motion Execution with a Mixture of Torque Controllers

This video shows the simulated motions for the humanoid robot COMAN. The initial mixture coefficients are compared to optimized coefficients for compensating external forces and task-priority rearrangement.

Paper link: http://ieeexplore.ieee.org...

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Soft catching an object in flight

Catching a fast flying object is particularly challenging as consists of two tasks: it requires extremely precise estimation of the object's motion and control of the robot motion. Any small imprecision may lead the fingers to close too abruptly and let the object fly away from the hand before closing. We present a strategy to overcome for sensori-motor imprecision by introducing softness in the catching approach. Soft catching consists of having the robot moves with the object for a short period of time, so as to leave more time for the fingers to close on the object. We use a dynamical systems (DS) based control law to generate the appropriate reach and follow motion, which is expressed as a Linear Parameter Varying (LPV) system. We propose a method to approximate the parameters of LPV systems using Gaussian Mixture Models, based on a set of kinematically feasible demonstrations generated by an off-line optimal control framework. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction.

Paper link: http://ieeexplore.ieee.org...

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Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty

Coordinated control strategies for multi-robot systems are necessary for tasks that cannot be executed by a single robot. This encompasses tasks where the workspace of the robot is too small or where the load is too heavy for one robot to handle. Using multiple robots makes the task feasible by extending the workspace and/or increase the payload of the overall robotic system. In this paper, we consider two instances of such task: a co-worker scenario in which a human hands over a large object to a robot; intercepting a large flying object. The problem is made difficult as the pick-up/intercept motions must take place while the object is in motion and because the object's motion is not deterministic. The challenge is then to adapt the motion of the robotic arms in coordination with one another and with the object. Determining the pick-up/intercept point is done by taking into account the workspace of the multi-arm system and is continuously recomputed to adapt to change in the object's trajectory. We propose a dynamical systems (DS) based control law to generate autonomous and synchronized motions for a multi-arm robot system in the task of reaching for a moving object. We show theoretically that the resulting DS coordinates the motion of the robots with each other and with the object, while the system remains stable. We validate our approach on a dual-arm robotic system and demonstrate that it can re-synchronize and adapt the motion of each arm in synchrony in a fraction of seconds, even when the motion of the object is fast and not accurately predictable.

Paper link: https://infoscience.epfl.ch...

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