7 papers
PhyMix: Towards Physically Consistent Single-Image 3D Indoor Scene Generation with Implicit--Explicit Optimization
Dongli Wu, Jingyu Hu, Ka-Hei Hui +4
Existing single-image 3D indoor scene generators often produce results that look visually plausible but fail to obey real-world physics, limiting their reliability in robotics, emb…
Learning Task-Invariant Properties via Dreamer: Enabling Efficient Policy Transfer for Quadruped Robots
Junyang Liang, Yuxuan Liu, Yabin Chang +5
Achieving quadruped robot locomotion across diverse and dynamic terrains presents significant challenges, primarily due to the discrepancies between simulation environments and rea…
ICAT: Incident-Case-Grounded Adaptive Testing for Physical-Risk Prediction in Embodied World Models
Zhenglin Lai, Sirui Huang, Yuteng Li +3
Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physical risk and severe consequenc…
UNeMo: Collaborative Visual-Language Reasoning and Navigation via a Multimodal World Model
Changxin Huang, Lv Tang, Zhaohuan Zhan +5
Vision-and-Language Navigation (VLN) requires agents to autonomously navigate complex environments via visual images and natural language instructions--remains highly challenging.…
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning
Changxin Huang, Junyang Liang, Yanbin Chang +2
Enabling a high-degree-of-freedom robot to learn specific skills is a challenging task due to the complexity of robotic dynamics. Reinforcement learning (RL) has emerged as a promi…
Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution
Changxin Huang, Yanbin Chang, Junfan Lin +3
The ability to autonomously explore and resolve tasks with minimal human guidance is crucial for the self-development of embodied intelligence. Although reinforcement learning meth…