ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion

Abstract

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion. We demonstrate the effectiveness of the approach in simulation in different complex scenarios with the quadruped robot Solo12.

References in corpus (2)

• Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
• Single-shot Foothold Selection and Constraint Evaluation for Quadruped Locomotion

Cited by in corpus (1)

• Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning