9 papers
ABot-N1: Toward a General Visual Language Navigation Foundation Model
Ruiyan Gong, Yingnan Guo, Junjun Hu +43
Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typica…
ReflectVLN: Training Vision-Language Navigation Agents with Reflective Reasoning
Jiahang Wang, Yirong Yang, Yanqing Zhu +4
Existing vision-language navigation methods often couple a VLM with waypoint decoders to produce multi-step action plans, but they typically lack an explicit closed-loop mechanism…
World Action Models: The Next Frontier in Embodied AI
Siyin Wang, Junhao Shi, Zhaoyang Fu +11
Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explic…
Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents
Xu Chen, Shichao Xie, Zhining Gu +7
Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory construction predominantly relies on a…
ABot-N0: Technical Report on the VLA Foundation Model for Versatile Embodied Navigation
Zedong Chu, Shichao Xie, Xiaolong Wu +41
Embodied navigation has long been fragmented by task-specific architectures. We introduce ABot-N0, a unified Vision-Language-Action (VLA) foundation model that achieves a ``Grand U…
AstraNav-World: World Model for Foresight Control and Consistency
Jintao Chen, Junjun Hu, Haochen Bai +11
Embodied navigation in open, dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time. We propose AstraNav-World, an end-t…
NavForesee: A Unified Vision-Language World Model for Hierarchical Planning and Dual-Horizon Navigation Prediction
Fei Liu, Shichao Xie, Minghua Luo +4
Embodied navigation for long-horizon tasks, guided by complex natural language instructions, remains a formidable challenge in artificial intelligence. Existing agents often strugg…
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Siyin Wang, Jinlan Fu, Feihong Liu +11
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many s…
Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning
Azizul Zahid, Jie Fan, Farong Wang +3
Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation lear…