6 papers
HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation
An Liu, Bingxi Liu, Hongyu Ding +6
Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has emerged: the robot queries a mu…
Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
Hongyu Ding, Sizhuo Zhang, Ziming Xu +13
Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environments it has never seen. The dom…
V-Dreamer: Automating Robotic Simulation and Trajectory Synthesis via Video Generation Priors
Songjia He, Zixuan Chen, Hongyu Ding +5
Training generalist robots demands large-scale, diverse manipulation data, yet real-world collection is prohibitively expensive, and existing simulators are often constrained by fi…
MoMaStage: Skill-State Graph Guided Planning and Closed-Loop Execution for Long-Horizon Indoor Mobile Manipulation
Chenxu Li, Zixuan Chen, Yetao Li +5
Indoor mobile manipulation (MoMA) enables robots to translate natural language instructions into physical actions, yet long-horizon execution remains challenging due to cascading e…
LaViRA: Language-Vision-Robot Actions Translation for Zero-Shot Vision Language Navigation in Continuous Environments
Hongyu Ding, Ziming Xu, Yudong Fang +6
LaViRA: Zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to navigate unseen environments based on natural language instructions withou…
SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas
Hongyu Ding, Xinyue Liang, Yudong Fang +7
In this paper, we propose SEA, a novel approach for active robot exploration through semantic map prediction and a reinforcement learning-based hierarchical exploration policy. Unl…