Learning Control from Raw Position Measurements

Clicks: 74
ID: 283133
2023
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Abstract
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured. This circumstance, if not adequately considered, can compromise the success of MBRL approaches. To cope with this problem, we define a velocity-free state formulation which consists of the collection of past positions and inputs. Then, VF-MC-PILCO uses Gaussian Process Regression to model the dynamics of the velocity-free state and optimizes the control policy through a particle-based policy gradient approach. We compare VF-MC-PILCO with our previous MBRL algorithm, MC-PILCO4PMS, which handles the lack of direct velocity measurements by modeling the presence of velocity estimators. Results on both simulated (cart-pole and UR5 robot) and real mechanical systems (Furuta pendulum and a ball-and-plate rig) show that the two algorithms achieve similar results. Conveniently, VF-MC-PILCO does not require the design and implementation of state estimators, which can be a challenging and time-consuming activity to be performed by an expert user.
Reference Key
romeres2023learning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fabio Amadio; Alberto Dalla Libera; Daniel Nikovski; Ruggero Carli; Diego Romeres
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2301.13183v1
Keywords
cs.lg cs.ro

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