CognitiveAISystems
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3DGraphLLM
3DGraphLLM Public3DGraphLLM is a model that uses a 3D scene graph and an LLM to perform 3D vision-language tasks.
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MIKASA-Robo
MIKASA-Robo PublicBenchmark for robotic tabletop manipulation memory-intensive tasks
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Dynamic-Neural-Potential-Field
Dynamic-Neural-Potential-Field PublicApproach where the repulsive potential in an MPC pipeline is estimated by a neural model.
Python 15
Repositories
- RATE Public
Official implementation of Recurrent Action Transformer with Memory, an offline RL agent with memory mechanisms. https://sites.google.com/view/rate-model/
CognitiveAISystems/RATE’s past year of commit activity - 3DGraphLLM Public
3DGraphLLM is a model that uses a 3D scene graph and an LLM to perform 3D vision-language tasks.
CognitiveAISystems/3DGraphLLM’s past year of commit activity - pogema-benchmark Public
This is an umbrella repository that contains links and information about all the tools and algorithms related to the POGEMA Benchmark.
CognitiveAISystems/pogema-benchmark’s past year of commit activity - pogema-toolbox Public
The POGEMA Toolbox is a comprehensive framework designed to facilitate the testing of learning-based approaches within the POGEMA environment. This toolbox offers a unified interface that enables the seamless execution of any learnable MAPF algorithm in POGEMA.
CognitiveAISystems/pogema-toolbox’s past year of commit activity - pogema Public
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
CognitiveAISystems/pogema’s past year of commit activity - Dynamic-Neural-Potential-Field Public
Approach where the repulsive potential in an MPC pipeline is estimated by a neural model.
CognitiveAISystems/Dynamic-Neural-Potential-Field’s past year of commit activity - MAPF-GPT Public
[AAAI-2025] This repository contains MAPF-GPT, a deep learning-based model for solving MAPF problems. Trained with imitation learning on trajectories produced by LaCAM, it generates actions under partial observability without heuristics or agent communication. MAPF-GPT excels on unseen instances and outperforms learnable state-of-the-art solvers
CognitiveAISystems/MAPF-GPT’s past year of commit activity - learn-to-follow Public
[AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.
CognitiveAISystems/learn-to-follow’s past year of commit activity
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