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

Martim Brandao, Kenji Hashimoto, Atsuo Takanishi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    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 large-scale 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
    Title of host publication2017 IEEE-RAS 17th International Conference on Humanoid Robotics, Humanoids 2017
    PublisherIEEE Computer Society
    Pages39-46
    Number of pages8
    VolumePart F134101
    ISBN (Electronic)9781538646786
    DOIs
    Publication statusPublished - 2017 Dec 22
    Event17th IEEE-RAS International Conference on Humanoid Robotics, Humanoids 2017 - Birmingham, United Kingdom
    Duration: 2017 Nov 152017 Nov 17

    Other

    Other17th IEEE-RAS International Conference on Humanoid Robotics, Humanoids 2017
    CountryUnited Kingdom
    CityBirmingham
    Period17/11/1517/11/17

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    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Hardware and Architecture
    • Human-Computer Interaction
    • Electrical and Electronic Engineering

    Cite this

    Brandao, M., Hashimoto, K., & Takanishi, A. (2017). SGD for robot motion? the effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks. In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics, Humanoids 2017 (Vol. Part F134101, pp. 39-46). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2017.8239535