PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；School of Mechanical Engineering and Automation, Beihang University（北京航空航天大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Li Auto（理想汽车）
Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios but struggle to generalize to long-tailed situations. Meanwhile, learning-based methods have yet to achieve superior performance over rule-based approaches in large-scale closed-loop scenarios. To address these issues, we propose PlanAgent, the first mid-to-mid planning system based on a Multi-modal Large Language Model (MLLM). MLLM is used as a cognitive agent to introduce human-like knowledge, interpretability, and common-sense reasoning into the closed-loop planning. Specifically, PlanAgent leverages the power of MLLM through three core modules. First, an Environment Transformation module constructs a Bird's Eye View (BEV) map and a lane-graph-based textual description from the environment as inputs. Second, a Reasoning Engine module introduces a hierarchical chain-of-thought from scene understanding to lateral and longitudinal motion instructions, culminating in planner code generation. Last, a Reflection module is integrated to simulate and evaluate the generated planner for reducing MLLM's uncertainty. PlanAgent is endowed with the common-sense reasoning and generalization capability of MLLM, which empowers it to effectively tackle both common and complex long-tailed scenarios. Our proposed PlanAgent is evaluated on the large-scale and challenging nuPlan benchmarks. A comprehensive set of experiments convincingly demonstrates that PlanAgent outperforms the existing state-of-the-art in the closed-loop motion planning task. Codes will be soon released.
Energy-Efficient Motion Planning for Multi-Modal Hybrid Locomotion
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；California Institute of Technology（加州理工学院）
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
Georgia Institute of Technology（佐治亚理工学院）；School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程学院）；SkyMul Inc（SkyMul公司）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model
School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Southeast University Nanjing Jiangbei New Area Innovation Research Institute（东南大学南京江滨新区创新研究院）；National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology（国防科技大学装备状态感知与智能支撑国家重点实验室）；School of Transportation, Shandong University of Science and Technology（山东科技大学交通学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
UniPilot: Enabling GPS-Denied Autonomy Across Embodiments
Department of Engineering Cybernetics, O. S. Bragstads Plass 2D, Norwegian University of Science and Technology (NTNU), Trondheim, Norway（工程 cybernetics 系，挪威科学技术大学（NTNU），特隆赫姆，挪威）
Efficient Path Planning and Tracking for Multi-Modal Legged-Aerial Locomotion Using Integrated Probabilistic Road Maps (PRM) and Reference Governors (RG)
California Institute of Technology（加州理工学院）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Northeastern University（东北大学）
Autel Robotics（Autel机器人公司）；Nanjing University（南京大学）；Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）
Lang2Manip: A Tool for LLM-Based Symbolic-to-Geometric Planning for Manipulation
Khalifa University Center for Autonomous Robotic Systems (KUCARS)（卡利法大学自主机器人系统中心）；Khalifa University（卡利法大学）；Institute of Industrial and Control Engineering (IOC)（工业与控制工程研究所）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
CogDrive: Cognition-Driven Multimodal Prediction-Planning Fusion for Safe Autonomy
Singapore-MIT Alliance for Research and Technology (SMART), Singapore（新加坡-麻省理工联合研究技术联盟）；Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA（麻省理工学院城市研究与规划系）；School of Vehicle and Mobility, Tsinghua University, China（清华大学车辆与移动系统学院）；Department of Mechanical Engineering, National University of Singapore, Singapore（新加坡国立大学机械工程系）；Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, China（同济大学交通工程重点实验室）
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
Tsinghua University（清华大学）；Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute for Advanced Study（高等研究院）；Massachusetts Institute of Technology（麻省理工学院）
LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Aalto University（阿尔托大学）；Nanjing University (Suzhou Campus)（南京大学苏州校区）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
