Resolved Motion Control for 3D Underactuated Bipedal Walking using Linear Inverted Pendulum Dynamics and Neural Adaptation

TL;DR

This paper introduces a novel control framework combining linear inverted pendulum models with neural adaptation to enable stable 3D underactuated bipedal walking, validated through simulation and hardware experiments.

Contribution

It proposes a neural-adaptive LIP-based controller for 3D bipedal robots that improves stability and reduces model mismatch effects, with real-time trajectory generation.

Findings

Achieved stable periodic walking in simulation and hardware.

Effective neural adaptation reduces model mismatch.

Real-time inverse kinematics enables independent joint control.

Abstract

We present a framework to generate periodic trajectory references for a 3D under-actuated bipedal robot, using a linear inverted pendulum (LIP) based controller with adaptive neural regulation. We use the LIP template model to estimate the robot's center of mass (CoM) position and velocity at the end of the current step, and formulate a discrete controller that determines the next footstep location to achieve a desired walking profile. This controller is equipped on the frontal plane with a Neural-Network-based adaptive term that reduces the model mismatch between the template and physical robot that particularly affects the lateral motion. Then, the foot placement location computed for the LIP model is used to generate task space trajectories (CoM and swing foot trajectories) for the actual robot to realize stable walking. We use a fast, real-time QP-based inverse kinematics algorithm that produces joint references from the task space trajectories, which makes the formulation independent of the knowledge of the robot dynamics. Finally, we implemented and evaluated the proposed approach in simulation and hardware experiments with a Digit robot obtaining stable periodic locomotion for both cases.