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20242026
most citedLaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner

1 citations · 2 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.RO2026

StratMamba: Strategic and Reactive Stream Partitioning for Path-Efficient LiDAR-Based Obstacle Avoidance

Hung-Chieh Wu, Xiaopan Zhang, Kasra Sinaei +6

This paper proposes StratMamba, a dual-stream Mamba-based temporal modeling architecture, to more efficiently capture long-horizon temporal dependencies required for robot navigati…

cs.RO2026

CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction

Xiaopan Zhang, Zejin Wang, Zhixu Li +2

To complete assignments provided by humans in natural language, robots must interpret commands, generate and answer relevant questions for scene understanding, and manipulate targe…

cs.RO2025

GUIDES: Guidance Using Instructor-Distilled Embeddings for Pre-trained Robot Policy Enhancement

Minquan Gao, Xinyi Li, Qing Yan +4

Pre-trained robot policies serve as the foundation of many validated robotic systems, which encapsulate extensive embodied knowledge. However, they often lack the semantic awarenes…

cs.RO2025

Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling

Jianpeng Yao, Xiaopan Zhang, Yu Xia +3

Mobile robots navigating in crowds trained using reinforcement learning are known to suffer performance degradation when faced with out-of-distribution scenarios. We propose that b…

cs.RO2024★ 1 cited

LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner

Xiaopan Zhang, Hao Qin, Fuquan Wang +2

Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks…

cs.RO2024★ 1 cited

SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning

Jianpeng Yao, Xiaopan Zhang, Yu Xia +3

Reinforcement learning (RL) enables social robots to generate trajectories without relying on human-designed rules or interventions, making it generally more effective than rule-ba…