未知环境中扩散源的自主搜索

Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space

Learn-to-Race Autonomous Racing Virtual Challenge hosted on www<dot>aicrowd<dot>com platform consisted of two tracks: Single and Multi Camera. Our UniTeam team was among the final winners in the Single Camera track. The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations. In our approach, we used the U-Net architecture for road segmentation, variational autocoder for encoding a road binary mask, and a nearest-neighbor search strategy that selects the best action for a given state. Our agent achieved an average…

HyPerNav：未知环境中面向目标导航的混合感知方法

HyPerNav: Hybrid Perception for Object-Oriented Navigation in Unknown Environment

针对未知环境目标导航单模态感知的局限，提出融合第一人称局部观测与俯视全局地图的HyPerNav方法，借助VLM能力提升导航效果，在仿真和真实场景均达SOTA，消融实验验证双模态均有贡献。

Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data

This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i)~studying how RL methods lear…

面向未知场景与环境的具元认知能力感知AI智能体（MUSE）

Competence-Aware AI Agents with Metacognition for Unknown Situations and Environments (MUSE)

针对当前自主智能体在新环境中适应能力有限的问题，提出整合自我评估与调控元认知过程的MUSE框架，通过世界建模和LLM两种实现提升能力感知与策略选择，有效解决新颖任务并减少对大量数据和大模型的依赖。

复杂环境中的自主导航

Autonomous Navigation in Complex Environments

本文提出基于CNN-DNN融合的导航控制器，利用模仿学习结合LiDAR与相机数据在地下救援仿真环境中导航寻目标，并通过蒙特卡洛验证其鲁棒性。

用于合成媒体检测和归因的自主自适应系统

Autonomous and Self-Adapting System for Synthetic Media Detection and Attribution

提出一种自主自适应合成媒体识别系统，通过可演化的嵌入空间和开放集识别策略，自动检测并归因已知与未知生成器，显著优于现有方法。

FH-DRL：结合指数-双曲前沿启发式与深度强化学习加速未知环境探索

FH-DRL: Exponential-Hyperbolic Frontier Heuristics with DRL for accelerated Exploration in Unknown Environments

针对未知环境自主探索中先验地图缺失的问题，提出FH-DRL框架，结合指数-双曲前沿启发式与TD3智能体，平衡近远探索收益并实时考虑环境特征，在模拟与真实场景中显著提升探索效率，优于现有方法。

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the…

用于灾难建图与受害者定位的自主机器人

Autonomous Robot for Disaster Mapping and Victim Localization

本文提出基于Turtlebot3和ROS Noetic的自主机器人系统，通过AprilTags定位受害者并生成未知环境地图，引入Cubature Kalman Filter和信息论探索算法提升搜救效率。

面向自主 AI 智能体的自发起开放世界学习

Self-Initiated Open World Learning for Autonomous AI Agents

本文提出自发起开放世界学习（SOL）理论框架，使自主 AI 智能体能够主动检测、刻画、适应并增量学习现实世界中的未知新事物。

Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. The work presented in this report contributes towards the state-of-the-art in UAV contro…

Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges

Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment. The promise demonstrated by RL-guided workflows in unraveling electronic design automation problems has encouraged hardware security researchers to utilize autonomous RL agents in solving domain-specific problems. From the perspective of hardware security, such autonomous agents are appealing as they can generate optimal actions in an unknown adversarial environment. On the other hand, the continued globalization of…