Autonomous learning of multiple, context-dependent tasks
Istituto di Scienze e Tecnologie della Cognizione (ISTC), Consiglio Nazionale delle Ricerce (CNR)（国家研究委员会认知科学与技术研究所）；Institute of Informatics, Federal University of Rio Grande do Sul (UFRGS)（南里奥格兰德联邦大学信息学研究所）
Vieri Giuliano Santucci, Davide Montella, Bruno Castro da Silva, Gianluca Baldassarre
When facing the problem of autonomously learning multiple tasks with reinforcement learning systems, researchers typically focus on solutions where just one parametrised policy per task is sufficient to solve them. However, in complex environments presenting different contexts, the same task might need a set of different skills to be solved. These situations pose two challenges: (a) to recognise the different contexts that need different policies; (b) quickly learn the policies to accomplish the same tasks in the new discovered contexts. These two challenges are even harder if faced within an open-ended learning framework where an agent has to autonomously discover the goals that it might accomplish in a given environment, and also to learn the motor skills to accomplish them. We propose a novel open-ended learning robot architecture, C-GRAIL, that solves the two challenges in an integrated fashion. In particular, the architecture is able to detect new relevant contests, and ignore irrelevant ones, on the basis of the decrease of the expected performance for a given goal. Moreover, the architecture can quickly learn the policies for the new contexts by exploiting transfer learning importing knowledge from already acquired policies. The architecture is tested in a simulated robotic environment involving a robot that autonomously learns to reach relevant target objects in the presence of multiple obstacles generating several different obstacles. The proposed architecture outperforms other models not using the proposed autonomous context-discovery and transfer-learning mechanisms.
Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning
National Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统国家重点实验室）
Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat
National Elite Institute of Engineering,Northwestern Polytechnical University（西北工业大学工程精英学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Forschungszentrum Informatik（弗劳恩霍夫信息技术研究中心）；Hochschule Esslingen（埃斯林根应用技术大学）；University of Michigan（密歇根大学）；Voxel51；Karlsruher Institut für Technologie（卡尔斯鲁厄理工学院）
CarPLAN: Context-Adaptive and Robust Planning with Dynamic Scene Awareness for Autonomous Driving
Department of Artificial Intelligence, Hanyang University（翰林大学人工智能系）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电子与计算机工程系）；Department of Automotive Engineering, Hanyang University（翰林大学汽车工程系）
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
The Hong Kong University of Science and Technology（香港科技大学）；Division of Emerging Interdisciplinary Areas（新兴跨学科领域）；Robotics and Autonomous Systems Thrust（机器人与自主系统方向）；Department of Electronic and Computer Engineering（电子与计算机工程系）
The Multi-Trip Time-Dependent Mix Vehicle Routing Problem for Hybrid Autonomous Shared Delivery Location and Traditional Door-to-Door Delivery Modes
Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；The University of Hong Kong（香港大学）
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
University of Science and Technology of China（中国科学技术大学）；Yinwang Intelligent Technology Co., Ltd（银网智能科技有限公司）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving
Department of Civil and Environmental Engineering, The Pennsylvania State University（宾夕法尼亚州立大学土木与环境工程系）；Department of Civil Engineering, Stony Brook University（石溪大学土木工程系）
Strategic Fusion of Vision Language Models: Shapley-Credited Context-Aware Dawid-Skene for Multi-Label Tasks in Autonomous Driving
Centre for Transport Engineering and Modelling, Department of Civil and Environmental Engineering, Imperial College London（交通工程与建模中心，土木与环境工程系，帝国理工学院伦敦分校）；ELM Europe（ELM欧洲）
Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment
Indian Institute of Science（印度科学学院）；Indian Institute of Science Education and Research, Bhopal（博帕尔印度科学教育与研究学院）；Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（印度科学学院罗伯特·博世网络物理系统中心）
