Learning locomotion over rough terrain using terrain templates

— We address the problem of foothold selection in robotic legged locomotion over very rough terrain. The difficulty of the problem we address here is comparable to that of human rock-climbing, where foot/hand-hold selection is one of the most critical aspects. Previous work in this domain typically involves defining a reward function over footholds as a weighted linear combination of terrain features. However, a significant amount of effort needs to be spent in designing these features in order to model more complex decision functions, and hand-tuning their weights is not a trivial task. We propose the use of terrain templates, which are discretized height maps of the terrain under a foothold on different length scales, as an alternative to manually designed features. We describe an algorithm that can simultaneously learn a small set of templates and a foothold ranking function using these templates, from expertdemonstrated footholds. Using the LittleDog quadruped robot, we experi...

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Mrinal Kalakrishnan, Jonas Buchli, Peter Pastor, Stefan Schaal
• Keywords: IROS 2009, Robotics, Terrain, Terrain Features, Terrain Templates