Learning for contact-rich tasks with cobots
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2024-09-20
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Master Thesis
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Abstract
A common approach to perform contact-rich tasks with robots is using force impedance control. The traditional method relies on commanding torque to the robot actuators. However, in modern cobots such interface is restricted due to safety reasons.
To address this limitation, we implement an outer control loop that uses information from an external force sensor mounted in the robot’s end-effector to command joint velocities in a way that effectively emulates an impedance behavior. Motivated by the challenging tuning of this controller, we explore a data-driven tuning method based on Bayesian Optimization.
Once robots operate in impedance mode, the next step is designing trajectories that achieve the desired tasks. We propose a strategy that uses Proximal Policy Optimization (PPO) to learn a motion pattern to perform a complex assembly without relying on a strong trajectory parametrization.
Overall, this work demonstrates a pipeline towards more autonomous procedures to enable cobots for contact-risk manipulations.
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Examiner: Lygeros, John
Examiner : Balta, Efe
Examiner: Micheli, Francesco
Examiner : Eberhard, Enrico
Examiner : Reber, Dominic
Examiner : Papaspyros, Vaios
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ETH Zurich
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03751 - Lygeros, John / Lygeros, John