SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World
This work is significant for robotics researchers and practitioners aiming to deploy humanoids in complex, unstructured human environments, offering a robust solution for vision-based traversal.
This paper addresses the challenge of enabling humanoids to traverse diverse real-world terrains using egocentric vision. The proposed SSR framework allows humanoids to achieve safe, stable, and high-quality locomotion on various terrains, including stairs, wide gaps, and high platforms, and enables reliable long-horizon traversal in open outdoor environments.
Extending humanoid traversal to the open world is key to practical deployment in human environments, but remains challenging. The robot must use vision to ensure safe and reliable foot placement on heterogeneous terrain under highly dynamic motion, while producing coordinated, natural whole-body behaviors. We propose SSR, an efficient end-to-end framework for egocentric vision-based humanoid traversal that jointly learns these capabilities. SSR introduces imagined foothold guidance, which learns to model forthcoming swing-foot contacts and evaluates their support to guide pre-touchdown swings toward stable regions, reducing edge slips. It further employs equivariant latent-space symmetry augmentation to efficiently induce bilateral coordination under high-dimensional visual observations, and uses terrain-specific multi-discriminator motion priors to encourage human-like behavior across scenes. Extensive experiments show that SSR achieves safe, stable, and high-quality locomotion on diverse real-world terrains, including stairs with varied structures and extreme challenges such as wide gaps and high platforms, while enabling reliable long-horizon traversal in open outdoor environments.