1 citations · 1 across the 5 of their papers we have counts for
7 papers
Spatial Grafting: Grounding 3D Features for Flow-Matching Robot Policies
Dingsheng Liu, Yangzheng Wu, Mahboubeh Asadi +5
Pretrained robot manipulation policies such as vision-language-action models (VLAs) or world-action models (WAMs) leave interaction-relevant metric geometry implicit. Recent breakt…
StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation
Jinbang Huang, Yuanzhao Hu, Zhiyuan Li +6
Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and adva…
RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning
Jinbang Huang, Yuanzhao Hu, Zhiyuan Li +6
Long-horizon robotic tasks require a breadth of capabilities beyond what any single existing robot control policy can reliably provide. Combining heterogeneous policies with comple…
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9
World models are becoming central to robotic planning and control by predicting future state transitions. Existing approaches mainly rely on visual, latent, or language prediction,…
Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4
Large Language Models (LLMs) have recently shown strong promise for robotic task planning, particularly through automatic planning domain generation. However, prior approaches larg…
One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
Jinbang Huang, Yixin Xiao, Zhanguang Zhang +3
Pre-trained large language models (LLMs) show promise for robotic task planning but often struggle to guarantee correctness in long-horizon problems. Task and motion planning (TAMP…
Automated Planning Domain Inference for Task and Motion Planning
Jinbang Huang, Allen Tao, Rozilyn Marco +3
Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP…