Terrain-Blind Humanoid Walking Based on a 3-D Actuated Dual-SLIP Model

Abstract

While a number of controllers exist for dynamic humanoid walking over known uneven terrain, the ability to negotiate moderate changes in ground height without environment perception is still lacking. Such capability would mitigate problems caused by inaccurate sensing and reduce online terrain-dependent computational requirements. This letter proposes a 1-step terrain adaptation strategy for humanoid walking based on the 3-D actuated Dual-SLIP model. A flexible gait to negotiate unknown terrain is synthesized from a series of consistent gait adaptations acquired from off-line optimization with this simple model. Being open-loop prior to touchdown, the strategy requires no perception of the terrain. Also, the resultant terrain-robust swing foot trajectory exhibits human-like characteristics such as leg retraction and extension near the end of the swing phase. Through a task-space control framework, the model-derived gait is embedded into the high-DoF ATLAS humanoid model. In the final result, a “blindfolded” ATLAS model reliably walks over randomly generated uneven terrain (with per-step height changes of up to 5% of the leg length) at a constant midstance speed.