Structured Techniques for Algorithmic Robotics (STAR) Lab
Open-source code from the STAR Lab at Georgia Tech. PI: Harish Ravichandar
United States of America
Pinned Repositories
cap-comm
Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities
CIMER
CMTAB
Robotics: Science and Systems (RSS) 2023
constrained-rl-dexterous-manipulation
IJCAI 2022 1st Safe RL Workshop paper
corrective-demos-dexterous-manipulation
HRI 2022 Workshop Paper
GRSTAPS
Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (IJRR 2022)
KODex
MARBLER
Realistic Benchmarks for Collaborative Heterogeneous Multi-Robot Systems
Q-ITAGS
Quality optimized task allocation and scheduling for muli-robot teams
Resource-Aware-Generalization
AAMAS 2022 - Extended Abstract
Structured Techniques for Algorithmic Robotics (STAR) Lab's Repositories
GT-STAR-Lab/KODex
GT-STAR-Lab/MARBLER
Realistic Benchmarks for Collaborative Heterogeneous Multi-Robot Systems
GT-STAR-Lab/cap-comm
Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities
GT-STAR-Lab/GRSTAPS
Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (IJRR 2022)
GT-STAR-Lab/CMTAB
Robotics: Science and Systems (RSS) 2023
GT-STAR-Lab/CIMER
GT-STAR-Lab/constrained-rl-dexterous-manipulation
IJCAI 2022 1st Safe RL Workshop paper
GT-STAR-Lab/corrective-demos-dexterous-manipulation
HRI 2022 Workshop Paper
GT-STAR-Lab/D-ITAGS
Dynamic Incremental Task Allocation Graph Search (RA-L / IROS 2023)
GT-STAR-Lab/gt-star-lab.github.io
Structured Techniques for Algorithmic Robotics (STAR) Lab at Georgia Tech. PI: Harish Ravichandar
GT-STAR-Lab/MARBLER-CA
MARBLER for Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities
GT-STAR-Lab/Q-ITAGS
Quality optimized task allocation and scheduling for muli-robot teams
GT-STAR-Lab/Resource-Aware-Generalization
AAMAS 2022 - Extended Abstract
GT-STAR-Lab/Continuous-CBS
Continuous CBS - a modification of conflict based search algorithm, that allows to perform actions (move, wait) of arbitrary duration. Timeline is not discretized, i.e. is continuous.
GT-STAR-Lab/JaxMARL_MARBLER
Multi-Agent Reinforcement Learning with JAX - Forked to add MARBLER
GT-STAR-Lab/K-CBS-Expert
Expert of the K-CBS algorithm we're planning to use for our VMAS environment
GT-STAR-Lab/risk_adaptive_task_allocation
IROS 2021 Paper
GT-STAR-Lab/trait_weight_optimization
AAMAS 2023 - Extended Abstract
GT-STAR-Lab/VectorizedMultiAgentSimulator
VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.