iWalker: Imperative Visual Planning for Walking Humanoid Robot

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.

Authors

• Xiao Lin
• Yuhao Huang
• Taimeng Fu
• Xiaobin Xiong
• Chen Wang

Keywords

• Legged locomotion
• Visualization
• Humanoid robots
• Self-supervised learning
• Robot sensing systems
• Planning
• Sensors
• Collision avoidance
• Optimization
• Intelligent robots
• Walking
• Humanoid Robot
• Simulation Environment
• Generalization Capability
• Real-world Environments
• Obstacle Avoidance
• Body Balance
• Bilevel Optimization
• Angular Velocity
• Kalman Filter
• Control Mode
• Step Length
• Path Planning
• Depth Images
• Inertial Measurement Unit
• Model Predictive Control
• Optimal Path
• Robotic Platform
• Local Frame
• Low-level Control
• Real Robot
• Simultaneous Localization And Mapping
• Proximal Policy Optimization
• Unicycle
• Visual Odometry
• Goal Position
• Software Architecture
• Inverted Pendulum Model
• Robot Control