Probabilistic Gradient Ascent with Applications to Bipedal Robot Locomotion

Paper Details

  • Title: Probabilistic Gradient Ascent with Applications to Bipedal Robot Locomotion
  • Authors:
    • David Budden
    • Josiah Walker
    • Madison Flannery
    • Alexandre Mendes
  • Keywords:
    • Robotics
    • Robotic Soccer
    • Optimisation
    • Bipedal Locomotion
    • Reinforcement Learning


Bipedal robotic locomotion is an emerging field within the multi-billion dollar robotics industry, with global initiatives (such as RoboCup, FIRA and the DARPA Robotics Challenge) striving toward the development of robots able to complete complex physical tasks within a human-engineered environment. This paper details the redevelopment of an omnidirectional walk engine for the DARwIn-OP,
with an improved online optimisation framework developed for 13 of its internal parameters. Applying two well-known optimisation algorithms within this framework yields significant improvement in walk speed and stability. A new non-convex optimisation algorithm (Probabilistic Gradient Ascent) is derived from a reinforcement learning framework and applied to the same task, yielding an average speed improvement of 50.4% and setting a new maximum speed benchmark of 34.1 cm/s.

Publication Details

  • Status: Accepted
  • Date Accepted: 17/010/2013
  • Conference: Australasian Conference on Robotics and Automation (ACRA) 2013

How to Cite (BibTeX)

  title={Probabilistic gradient ascent with applications to bipedal robot locomotion},
  author={Budden, D. and Walker, J. and Flannery, M. and Mendes, A.},
  booktitle={Australasian Conference on Robotics and Automation (ACRA)},

Supplementary Materials

  • Original manuscript available here
  • The following video provides a demonstration of the experimental results