9 papers
Does Step Law Transfer to Small-Scale Language Models? An Empirical Recalibration Below 59M Parameters
Egor Romanyukov, Timofey Novikov, Timur Shokarov +3
Step Law gives power-law formulas for the optimal peak learning rate eta* and batch size B* when pre-training language models. It was calibrated on models between 59M and 1B parame…
CoRL-MPPI: Enhancing MPPI With Learnable Behaviours For Efficient And Provably-Safe Multi-Robot Collision Avoidance
Stepan Dergachev, Artem Pshenitsyn, Aleksandr Panov +2
Decentralized collision avoidance is a core challenge for scalable multi-robot systems. A promising approach to this problem is Model Predictive Path Integral (MPPI) control - a fr…
Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral
Stepan Dergachev, Konstantin Yakovlev
Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for b…
Decentralized Unlabeled Multi-Agent Navigation in Continuous Space
Stepan Dergachev, Konstantin Yakovlev
In this work, we study the problem where a group of mobile agents needs to reach a set of goal locations, but it does not matter which agent reaches a specific goal. Unlike most of…
Decentralized Unlabeled Multi-agent Pathfinding Via Target And Priority Swapping (With Supplementary)
Stepan Dergachev, Konstantin Yakovlev
In this paper we study a challenging variant of the multi-agent pathfinding problem (MAPF), when a set of agents must reach a set of goal locations, but it does not matter which ag…
2.5D Mapping, Pathfinding and Path Following For Navigation Of A Differential Drive Robot In Uneven Terrain
Stepan Dergachev, Kirill Muravyev, Konstantin Yakovlev
Safe navigation in uneven terrains is an important problem in robotic research. In this paper we propose a 2.5D navigation system which consists of elevation map building, path pla…
Towards A Complete Multi-Agent Pathfinding Algorithm For Large Agents
Stepan Dergachev, Konstantin Yakovlev
Multi-agent pathfinding (MAPF) is a challenging problem which is hard to solve optimally even when simplifying assumptions are adopted, e.g. planar graphs (typically -- grids), dis…
Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Finding
Stepan Dergachev, Konstantin Yakovlev
Avoiding collisions is the core problem in multi-agent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typica…
A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation
Stepan Dergachev, Konstantin Yakovlev, Ryhor Prakapovich
We study the problem of multi-agent navigation in static environments when no centralized controller is present. Each agent is controlled individually and relies on three algorithm…