Search Results for author:
Found 15 papers, 10 papers with code
Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding
Our approach utilizes the agent's observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS.
Interactive Semantic Map Representation for Skill-based Visual Object Navigation
Visual object navigation using learning methods is one of the key tasks in mobile robotics.
Neural Potential Field for Obstacle-Aware Local Motion Planning
Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning.
SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment
This paper presents an adaptive transformer model named SegmATRon for embodied image semantic segmentation.
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
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.
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints.
Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results
We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem.
Reinforcement Learning with Success Induced Task Prioritization
Many challenging reinforcement learning (RL) problems require designing a distribution of tasks that can be applied to train effective policies.
TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers
To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search.
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Human intelligence can remarkably adapt quickly to new tasks and environments.
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
Pathfinding in stochastic environments: learning vs planning
Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
