MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration

Yishuai Cai, Xinglin Chen, Zhongxuan Cai, Yunxin Mao, Minglong Li, Wenjing Yang, Ji Wang

AAAI 2025 Oral | Citations: 8

• Proposes a multi-robot behavior tree planning algorithm with theoretical soundness and completeness guarantees. Achieves efficient multi-agent collaboration through cross-tree expansion and intention sharing.

Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions

Xinglin Chen^*, Yishuai Cai^*, Yunxin Mao, Minglong Li, Wenjing Yang, Weixia Xu, Ji Wang

IJCAI 2024 | Citations: 31

• Proposes a two-stage behavior tree generation framework that integrates large language model understanding with optimal behavior tree expansion, generating semantically correct and task-feasible behavior trees from natural language instructions.

CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Yishuai Cai, Xinglin Chen, Yunxin Mao, Kun Hu, Minglong Li, Yaodong Yang, Yuanpei Chen

AAAI 2026 | Citations: -

• Proposes a context-aware behavior tree grounding approach that effectively grounds high-level semantic instructions into low-level robot-executable control trees, enabling real-world robot manipulation.

VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models

Borong Zhang, Jiahao Li, Jiachen Shen, Yishuai Cai, Yuhao Zhang, Yuanpei Chen, Juntao Dai, Jiaming Ji, Yaodong Yang

arXiv | Citations: 5

• Proposes an open-source benchmark framework for systematic evaluation of Vision-Language-Action models across dimensions of safety, generalization, and task complexity, supporting fine-grained difficulty level design.

DexFlyWheel: A Scalable and Self-improving Data Generation Framework for Dexterous Manipulation

Kefei Zhu, Fengshuo Bai, YuanHao Xiang, Yishuai Cai, Xinglin Chen, Ruochong Li, Xingtao Wang, Hao Dong, Yaodong Yang^†, Xiaopeng Fan^†, Yuanpei Chen^†

NeurIPS 2025 Spotlight (Top 3.2%) | Citations: 5

• Proposes a scalable self-improving data generation framework that efficiently generates high-quality dexterous manipulation training data through robot demonstration collection and self-supervised learning cycles.

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

Borong Zhang, Yuhao Zhang, Jiaming Ji, Yingshan Lei, Josef Dai, Yuanpei Chen, Yaodong Yang

NeurIPS 2025 Spotlight (Top 3.2%) | Citations: 24

• Proposes an Integrated Safety Approach that systematically models safety requirements, actively elicits unsafe behaviors, and enforces safety constraints via constrained MDPs for safe reinforcement learning, achieving safety alignment of Vision-Language-Action models.

HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning

Yishuai Cai, Xinglin Chen, Yunxin Mao, Minglong Li, Shaowu Yang, Wenjing Yang, Ji Wang

ICRA 2025 | Citations: 2

• Proposes a heuristic behavior tree planning framework that leverages large language model reasoning to generate effective heuristics, significantly improving search efficiency and solution quality.

Task2Morph: Differentiable Task-inspired Framework for Contact-Aware Robot Design

Yishuai Cai, Shaowu Yang, Minglong Li, Xinglin Chen, Yunxin Mao, Xiaodong Yi, Wenjing Yang

IROS 2023 | Citations: 4

Evolving Physical Instinct for Morphology and Control Co-Adaption

Xinglin Chen, Da Huang, Minglong Li, Yishuai Cai, Zhuoer Wen, Zhongxuan Cai, Wenjing Yang

IROS 2023 | Citations: 5