English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a method for identifying mechanical parameters of robotsor objects, such as their mass and friction coefficients. Key features are theuse of off-the-shelf physics engines and the adaptation of a Bayesianoptimization technique towards minimizing the number of real-world experimentsneeded for model-based reinforcement learning. The proposed frameworkreproduces in a physics engine experiments performed on a real robot andoptimizes the model's mechanical parameters so as to match real-worldtrajectories. The optimized model is then used for learning a policy insimulation, before real-world deployment. It is well understood, however, thatit is hard to exactly reproduce real trajectories in simulation. Moreover, anear-optimal policy can be frequently found with an imperfect model. Therefore,this work proposes a strategy for identifying a model that is just good enoughto approximate the value of a locally optimal policy with a certain confidence,instead of wasting effort on identifying the most accurate model. Evaluations,performed both in simulation and on a real robotic manipulation task, indicatethat the proposed strategy results in an overall time-efficient, integratedmodel identification and learning solution, which significantly improves thedata-efficiency of existing policy search algorithms.

Fast Model Identification via Physics Engines for Data-Efficient Policy Search