SGD for robot motion? the effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks

Martim Brandão, Kenji Hashimoto, Atsuo Takanishi

Research output: Contribution to journalArticlepeer-review

Abstract

Trajectory optimization and posture generation are hard problems in robot locomotion, which can be nonconvex and have multiple local optima. Progress on these problems is further hindered by a lack of open benchmarks, since comparisons of different solutions are difficult to make. In this paper we introduce a new benchmark for trajectory optimization and posture generation of legged robots, using a pre-defined scenario, robot and constraints, as well as evaluation criteria. We evaluate state-of-the-art trajectory optimization algorithms based on sequential quadratic programming (SQP) on the benchmark, as well as new stochastic and incremental optimization methods borrowed from the largescale machine learning literature. Interestingly we show that some of these stochastic and incremental methods, which are based on stochastic gradient descent (SGD), achieve higher success rates than SQP on tough initializations. Inspired by this observation we also propose a new incremental variant of SQP which updates only a random subset of the costs and constraints at each iteration. The algorithm is the best performing in both success rate and convergence speed, improving over SQP by up to 30% in both criteria. The benchmark's resources and a solution evaluation script are made openly available.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2017 Oct 9

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'SGD for robot motion? the effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks'. Together they form a unique fingerprint.

Cite this