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
• Recovery reflexes

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.

Videos

(:youtube nUQsRPJ1dYw :)

Publications

(:clmckeywordsearch littledog:)