iWalker: Imperative Visual Planning for Walking Humanoid Robot

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.

References in corpus (12)

• OSQP: An Operator Splitting Solver for Quadratic Programs
• From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots
• Learning High-Speed Flight in the Wild
• Lio -- A Personal Robot Assistant for Human-Robot Interaction and Care Applications
• AirVO: An Illumination-Robust Point-Line Visual Odometry
• iPlanner: Imperative Path Planning
• PyPose: A Library for Robot Learning with Physics-based Optimization
• iSLAM: Imperative SLAM
• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
• iA*: Imperative Learning-based A* Search for Path Planning
• iMatching: Imperative Correspondence Learning
• iKap: Kinematics-aware Planning with Imperative Learning