Reachability Map for Diverse Balancing Strategies and Energy Efficient Stepping of Humanoids

TL;DR

This paper introduces a nonlinear optimization approach to generate reachability maps for humanoid robots, revealing complex energy-stepping relationships and the non-uniqueness of near-optimal balance recovery strategies.

Contribution

It develops a 2-phase nonlinear optimization pipeline for dynamic stepping and energy analysis, providing new insights into energy-efficient gait planning and diverse stepping behaviors.

Findings

Reachability maps illustrate complex energy-stepping relations.

Near-minimal effort steps are within attraction regions, not narrow solutions.

Diverse near-optimal solutions exist for balance recovery.

Abstract

In legged locomotion, the relationship between different gait behaviors and energy consumption must consider the full-body dynamics and the robot control as a whole, which cannot be captured by simple models. This work studies the robot dynamics and whole-body optimal control as a coupled system to investigate energy consumption during balance recovery. We developed a 2-phase nonlinear optimization pipeline for dynamic stepping, which generates reachability maps showing complex energy-stepping relations. We optimize gait parameters to search all reachable locations and quantify the energy cost during dynamic transitions, which allows studying the relationship between energy consumption and stepping locations given different initial conditions. We found that to achieve efficient actuation, the stepping location and timing can have simple approximations close to the underlying optimality. Despite the complexity of this nonlinear process, we show that near-minimal effort stepping locations fall within a region of attractions, rather than a narrow solution space suggested by a simple model. This provides new insights into the non-uniqueness of near-optimal solutions in robot motion planning and control, and the diversity of stepping behavior in humans.