No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain

2025-08-16

AI Summary

To address the challenges of omnidirectional bipedal locomotion in dynamic, cluttered indoor environments and on uneven terrain, this paper proposes a rendering-free, vision-driven reinforcement learning framework. Our method eliminates computationally expensive omnidirectional depth-image simulation by integrating blind-control priors with a teacher–student distillation mechanism, enabling efficient training of a lightweight visual controller using noise-augmented photorealistic synthetic data. This work presents the first approach for omnidirectional bipedal walking grounded in full-field depth perception, while enabling high-fidelity sim-to-real transfer. Experiments demonstrate stable omnidirectional locomotion across diverse complex terrains; training speed improves tenfold; reliance on high-fidelity rendering is significantly reduced; and policy robustness and generalization capability are substantially enhanced.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Effective bipedal locomotion in dynamic environments, such as cluttered indoor spaces or uneven terrain, requires agile and adaptive movement in all directions. This necessitates omnidirectional terrain sensing and a controller capable of processing such input. We present a learning framework for vision-based omnidirectional bipedal locomotion, enabling seamless movement using depth images. A key challenge is the high computational cost of rendering omnidirectional depth images in simulation, making traditional sim-to-real reinforcement learning (RL) impractical. Our method combines a robust blind controller with a teacher policy that supervises a vision-based student policy, trained on noise-augmented terrain data to avoid rendering costs during RL and ensure robustness. We also introduce a data augmentation technique for supervised student training, accelerating training by up to 10 times compared to conventional methods. Our framework is validated through simulation and real-world tests, demonstrating effective omnidirectional locomotion with minimal reliance on expensive rendering. This is, to the best of our knowledge, the first demonstration of vision-based omnidirectional bipedal locomotion, showcasing its adaptability to diverse terrains.

Problem

• Develop vision-based omnidirectional bipedal locomotion for challenging terrain
• Overcome high computational cost of omnidirectional depth image rendering
• Ensure robustness and adaptability with noise-augmented training data

Innovation

• Vision-based omnidirectional locomotion learning
• Blind controller with teacher-student policy
• Noise-augmented terrain data for training