2 citations · 3 across the 10 of their papers we have counts for
10 papers
Model-based Policy Optimization using Symbolic World Model
Andrey Gorodetskiy, Konstantin Mironov, Aleksandr Panov
The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with lo…
Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments
Zoya Volovikova, Alexey Skrynnik, Petr Kuderov +1
In this study, we address the issue of enabling an artificial intelligence agent to execute complex language instructions within virtual environments. In our framework, we assume t…
Interactive Semantic Map Representation for Skill-based Visual Object Navigation
Tatiana Zemskova, Aleksei Staroverov, Kirill Muravyev +2
Visual object navigation using learning methods is one of the key tasks in mobile robotics. This paper introduces a new representation of a scene semantic map formed during the emb…
Neural Potential Field for Obstacle-Aware Local Motion Planning
Muhammad Alhaddad, Konstantin Mironov, Aleksey Staroverov +1
Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case…
SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment
Tatiana Zemskova, Margarita Kichik, Dmitry Yudin +2
This paper presents an adaptive transformer model named SegmATRon for embodied image semantic segmentation. Its distinctive feature is the adaptation of model weights during infere…
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Alexey Skrynnik, Anton Andreychuk, Maria Nesterova +2
Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.…