iWalker: Imperative Visual Planning for Walking Humanoid Robot

TL;DR

iWalker introduces an end-to-end vision-based humanoid walking system using imperative learning, enabling obstacle avoidance and balance through self-supervised bilevel optimization, improving adaptability in real-world environments.

Contribution

The paper presents a novel end-to-end system with imperative learning for humanoid walking, integrating vision-based planning and self-supervised bilevel optimization for the first time.

Findings

Effective obstacle avoidance demonstrated in simulation and real-world tests.

Self-supervised learning enhances adaptability and generalization.

Improved balance and footstep planning accuracy.

Abstract

Humanoid robots, designed to operate in human-centric environments, serve as a fundamental platform for a broad range of tasks. Although humanoid robots have been extensively studied for decades, a majority of existing humanoid robots still heavily rely on complex modular frameworks, leading to inflexibility and potential compounded errors from independent sensing, planning, and acting components. In response, we propose an end-to-end humanoid sense-plan-act walking system, enabling vision-based obstacle avoidance and footstep planning for whole body balancing simultaneously. We designed two imperative learning (IL)-based bilevel optimizations for model-predictive step planning and whole body balancing, respectively, to achieve self-supervised learning for humanoid robot walking. This enables the robot to learn from arbitrary unlabeled data, improving its adaptability and generalization capabilities. We refer to our method as iWalker and demonstrate its effectiveness in both simulated and real-world environments, representing a significant advancement toward autonomous humanoid robots.