8 citations · 11 across the 8 of their papers we have counts for
9 papers
ROAR: Benchmarking LMM Agents under Real-Time Constraints in Multiplayer Action-Shooter Games
Dawei Wang, Di Zhao, Jianran Yu +8
Large multimodal models (LMMs) are increasingly deployed as agents that perceive, reason, and act in interactive multi-agent environments. Yet most existing benchmarks for LMM-base…
MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation
Dawei Wang, Di Zhao, Xinyuan Liu +5
Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback a…
TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents
Dawei Wang, Chengming Zhou, Di Zhao +4
Recent breakthroughs in Large Language Models (LLMs) have positioned them as a promising paradigm for agents, with long-term planning and decision-making emerging as core general-p…
PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models
Jiansong Wan, Chengming Zhou, Jinkua Liu +14
Recent studies have explored pretrained (foundation) models for vision-based robotic navigation, aiming to achieve generalizable navigation and positive transfer across diverse env…
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
Chengmin Zhou, Ville Kyrki, Pasi Fränti +1
Bayesian inference has many advantages in decision making of agents (e.g. robotics/simulative agent) over a regular data-driven black-box neural network: Data-efficiency, generaliz…
Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning
Chengmin Zhou, Xin Lu, Jiapeng Dai +3
Motion planning is the soul of robot decision making. Classical planning algorithms like graph search and reaction-based algorithms face challenges in cases of dense and dynamic ob…
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
Chengmin Zhou, Chao Wang, Haseeb Hassan +3
Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees o…
An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios
Chengmin Zhou, Bingding Huang, Pasi Fränti
Intelligent robots provide a new insight into efficiency improvement in industrial and service scenarios to replace human labor. However, these scenarios include dense and dynamic…
A review of motion planning algorithms for intelligent robotics
Chengmin Zhou, Bingding Huang, Pasi Fränti
We investigate and analyze principles of typical motion planning algorithms. These include traditional planning algorithms, supervised learning, optimal value reinforcement learnin…