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LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception
Yukun Luo, Jianjun Ma, Yuyao Min +3
Humanoid local navigation in cluttered environments must jointly resolve obstacle avoidance, sparse-goal recovery, and stable whole-body locomotion under short-range and partially…
SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints
Yantong Wei, Kaihong Huang, Hainan Pan +6
The pursuit of humanoid athletic sprints is hindered by a scarcity of humanoid-viable kinematic reference data and the inability of existing frameworks to maintain stability during…
GeoLoco: Leveraging 3D Geometric Priors from Visual Foundation Model for Robust RGB-Only Humanoid Locomotion
Yufei Liu, Xieyuanli Chen, Hainan Pan +5
The prevailing paradigm of perceptive humanoid locomotion relies heavily on active depth sensors. However, this depth-centric approach fundamentally discards the rich semantic and…
Efficient Image-Goal Navigation with Representative Latent World Model
Zhiwei Zhang, Hui Zhang, Kaihong Huang +2
World models enable robots to conduct counterfactual reasoning in physical environments by predicting future world states. While conventional approaches often prioritize pixel-leve…
SurfAAV: Design and Implementation of a Novel Multimodal Surfing Aquatic-Aerial Vehicle
Kun Liu, Junhao Xiao, Hao Lin +4
Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement simultan…
Deep Reinforcement Learning for Flipper Control of Tracked Robots
Hainan Pan, Bailiang Chen, Kaihong Huang +3
The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely…
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
Wenbang Deng, Kaihong Huang, Qinghua Yu +3
Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these…