5 papers
Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning
Aditya Narendra, Mukhammadrizo Maribjonov, Dmitry Makarov +2
This paper introduces Knowledge Graph based Massively Multi-task Model-based Policy Optimization (KG-M3PO), a framework for multi-task robotic manipulation in partially observable…
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov +1
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment.…
M3PO: Massively Multi-Task Model-Based Policy Optimization
Aditya Narendra, Dmitry Makarov, Aleksandr Panov
We introduce Massively Multi-Task Model-Based Policy Optimization (M3PO), a scalable model-based reinforcement learning (MBRL) framework designed to address sample inefficiency in…
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning
Nikita Oskolkov, Huzhenyu Zhang, Dmitry Makarov +2
The 3D scene graph models spatial relationships between objects, enabling the agent to efficiently navigate in a partially observable environment and predict the location of the ta…
TOCALib: Optimal control library with interpolation for bimanual manipulation and obstacles avoidance
Yulia Danik, Dmitry Makarov, Aleksandra Arkhipova +2
The paper presents a new approach for constructing a library of optimal trajectories for two robotic manipulators, Two-Arm Optimal Control and Avoidance Library (TOCALib). The opti…