2 citations · 3 across the 6 of their papers we have counts for
9 papers
Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise
Zhen Huang, Zhihuang Liu, Mengxuan Luo +2
Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task planning for both single robot…
Topology-Guided ORCA: Smooth Multi-Agent Motion Planning in Constrained Environments
Fatemeh Cheraghi Pouria, Zhe Huang, Ananya Yammanuru +2
We present Topology-Guided ORCA as an alternative simulator to replace ORCA for planning smooth multi-agent motions in environments with static obstacles. Despite the impressive pe…
LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
Zhe Huang, John Pohovey, Ananya Yammanuru +1
Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when…
Learning Task Skills and Goals Simultaneously from Physical Interaction
Haonan Chen, Ye-Ji Mun, Zhe Huang +4
In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although…
Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints
Zhe Huang, Hongyu Chen, John Pohovey +1
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing…
Towards Safe Multi-Level Human-Robot Interaction in Industrial Tasks
Zhe Huang, Ye-Ji Mun, Haonan Chen +7
Multiple levels of safety measures are required by multiple interaction modes which collaborative robots need to perform industrial tasks with human co-workers. We develop three in…