Junli Ren

Towards Adaptable Humanoid Control Via Adaptive Motion Tracking

VB-Com: Learning Vision-Blind Composite Humanoid Locomotion against Deficient Perception

BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds

Traversing risky terrains with sparse footholds poses a significant challenge for humanoid robots, requiring precise foot placements and stable locomotion. Existing approaches designed for quadrupedal robots often fail to generalize to humanoid robots due to differences in foot geometry and unstable…

Learning Humanoid Locomotion with Perceptive Internal Model

In contrast to quadruped robots that can navigate diverse terrains using a “blind” policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of freedom and inherently unstable morphology. However, incorporating perceptual signals often introduces additional

Learning Humanoid Standing-up Control across Diverse Postures

Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems. Existing approaches are either limited to simulations that neglect hardware constraints or rely on predefined ground-specific motion trajectories, failing to…

TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation

Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obsta…