8 papers
EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ
Yunsong Zhou, Xuekun Jiang, Haiqi Shen +11
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold. Scaling collection…
Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping
Songze Li, Haoming Gu, Ziyang Gong +11
Vision-language navigation requires agents to follow natural-language instructions to reach target locations, making high-quality language supervision essential. Since human annota…
SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations
Jun Guo, Xiaoshen Han, Qiwei Li +7
World action models (WAMs) are large embodied policies that jointly predict future video and the actions to execute, emitting a fixed-length action chunk per inference call. Such a…
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization
Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26
Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…
AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
Jisong Cai, Long Ling, Shiwei Chu +10
World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.…
PALo: Learning Posture-Aware Locomotion for Quadruped Robots
Xiangyu Miao, Jun Sun, Hang Lai +4
With the rapid development of embodied intelligence, locomotion control of quadruped robots on complex terrains has become a research hotspot. Unlike traditional locomotion control…
RHINO: Learning Real-Time Humanoid-Human-Object Interaction from Human Demonstrations
Jingxiao Chen, Xinyao Li, Jiahang Cao +7
Humanoid robots have shown success in locomotion and manipulation. Despite these basic abilities, humanoids are still required to quickly understand human instructions and react ba…
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots
Peilin Wu, Weiji Xie, Jiahang Cao +2
Reinforcement Learning (RL) has shown its remarkable and generalizable capability in legged locomotion through sim-to-real transfer. However, while adaptive methods like domain ran…