Aleksandr I. Panov
МФТИ
- Reinforcement Learning in Robotics
- Robotic Path Planning Algorithms
- Multimodal Machine Learning Applications
- AI-based Problem Solving and Planning
- Robotics and Sensor-Based Localization
Чем занимается
Ключевые темы по публикациям: Reinforcement Learning in Robotics; Robotic Path Planning Algorithms; Multimodal Machine Learning Applications; AI-based Problem Solving and Planning; Robotics and Sensor-Based Localization.
Последние работы
Все 203 в OpenAlexGrid Path Planning with Deep Reinforcement Learning: Preliminary Results2018 · аннотациясвернуть
Single-shot grid-based path finding is an important problem with the applications in robotics, video games etc. Typically in AI community heuristic search methods (based on A* and its variations) are used to solve it.…
Real-Time Object Navigation With Deep Neural Networks and Hierarchical Reinforcement Learning2020 · аннотациясвернуть
In the last years, deep learning and reinforcement learning methods have significantly improved mobile robots in such fields as perception, navigation, and planning. But there are still gaps in applying these methods to…
Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection2019 · аннотациясвернуть
Abstract Among a number of problems in the behavior planning of an unmanned vehicle the central one is movement in difficult areas. In particular, such areas are intersections at which direct interaction with other road…
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- Learn to Follow: Decentralized Lifelong Multi-Agent Pathfinding via Planning and Learning2024
- Symbolic Disentangled Representations for Images2026
- Say it better: RL-based prompt tuning for enhancing open-vocabulary recognition2026
- Optimizing the Trajectory of Robotic Manipulator: Reinforcement Learning for the Generation of Initial Guess2026
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Шаблон письма
Здравствуйте, Aleksandr I. Panov! Я студент(ка) [курс, факультет, вуз]. Мне интересна тема [опишите интересы своими словами]. Прочитал(а) вашу работу «Grid Path Planning with Deep Reinforcement Learning: Preliminary Results» (2018) — она близка к тому, чем я хочу заниматься. Хочу обсудить возможность выполнить научную работу под вашим руководством. Буду благодарен(на) за ответ — готов(а) рассказать о себе подробнее и прислать резюме. С уважением, [Имя Фамилия]
На этапе теста письмо отправляешь сам из своей почты. Отправка из сервиса и статусы заявок — скоро.