The goal of the Learning Locomotion Project was to use machine learning techniques to create autonomous control software for a robot quadruped such that it can traverse unknown rugged and complex terrains. As experimental platform, the LittleDog robot was chosen, which is about 30cm long and 20cm high, with three degrees of freedom per leg. The specifications for the project required that the robot should achieve a speed of at least 7.2 cm/s and climb over obstacles up to 10.7cm (for humans, this would correspond to obstacles of 50% body height which are traversed at slow walking speed). For additional information, follow the link to this article and the official project website.
The technical components of our approach included:
Bayesian Learning for state estimation and outlier removal
Imitation learning of behaviors
Reinforcement learning for behavior improvement
Learning foothold templates for planning where to step
Compliant floating base inverse dynamics control with force control at the feet
Predictive control of contact forces
Convex optimization for ZMP balancer
High speed semi-dynamic walking gait
Below are publications that describe more details of our research. The ICRA 2010 paper was the Overall Best Paper Award Finalist, i.e., among the best 4 papers out of 2000 submissions.
Designed by: Nerses Ohanyan & Jan Peters
Page last modified on September 07, 2011, at 11:33 AM