2 citations · 3 across the 4 of their papers we have counts for
6 papers
SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining
Jicong Ao, Shuhan Jiang, Yuling Zhong +7
The ability to interact with articulated objects is essential for embodied intelligent systems, but collecting large-scale real-world demonstrations for these interactions remains…
AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly
Zhi Jing, Jinbin Qiao, Ouyang Lu +5
Spatial reasoning is a fundamental capability for embodied intelligence, especially for fine-grained manipulation tasks such as robotic assembly. Recent methods based on vision-lan…
Beyond Short-Horizon: VQ-Memory for Robust Long-Horizon Manipulation in Non-Markovian Simulation Benchmarks
Honghui Wang, Zhi Jing, Jicong Ao +4
The high cost of collecting real-robot data has made robotic simulation a scalable platform for both evaluation and data generation. Yet most existing benchmarks concentrate on sim…
HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning
Zhi Jing, Siyuan Yang, Jicong Ao +3
For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and…
Behavior Tree Generation using Large Language Models for Sequential Manipulation Planning with Human Instructions and Feedback
Jicong Ao, Yansong Wu, Fan Wu +1
In this work, we propose an LLM-based BT generation framework to leverage the strengths of both for sequential manipulation planning. To enable human-robot collaborative task plann…
LLM-as-BT-Planner: Leveraging LLMs for Behavior Tree Generation in Robot Task Planning
Jicong Ao, Fan Wu, Yansong Wu +2
Robotic assembly tasks remain an open challenge due to their long horizon nature and complex part relations. Behavior trees (BTs) are increasingly used in robot task planning for t…