A DataEfficient Approach to Precise and Controlled Pushing
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Proceedings of The 2nd Conference on Robot Learning, PMLR 87:336345, 2018.
Abstract
Decades of research in control theory have shown that simple controllers, when provided with timely feedback, can control complex systems. Pushing is an example of a complex mechanical system that is difficult to model accurately due to unknown system parameters such as coefficients of friction and pressure distributions. In this paper, we explore the datacomplexity required for controlling, rather than modeling, such a system. Results show that a modelbased control approach, where the dynamical model is learned from data, is capable of performing complex pushing trajectories with a minimal amount of training data (<10 data points). The dynamics of pushing interactions are modeled using a Gaussian process (GP) and are leveraged within a model predictive control approach that linearizes the GP and imposes actuator and task constraints for a planar manipulation task.
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