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gym-gazebo2, a toolkit for reinforcement learning using ros 2 and gazebo

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

This paper presents an upgraded, real world application oriented version of gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement Learning (RL) toolkit, which complies with OpenAI Gym. The content discusses the new ROS 2 based software architecture and summarizes the results obtained using Proximal Policy Optimization (PPO). Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions. We have evaluated environments with different levels of complexity of the Modular Articulated Robotic Arm (MARA), reaching accuracies in the millimeter scale. The converged results show the feasibility and usefulness of the gym-gazebo 2 toolkit, its potential and applicability in industrial use cases, using modular robots.

fields

cs.MA 1 cs.RO 1

years

2026 1 2025 1

verdicts

UNVERDICTED 2

representative citing papers

ARMATA: Auto-Regressive Multi-Agent Task Assignment

cs.MA · 2026-05-05 · unverdicted · novelty 5.0

ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.

citing papers explorer

Showing 2 of 2 citing papers.

  • RouteFormer: A Transformer-Based Routing Framework for Autonomous Vehicles cs.RO · 2025-04-07 · unverdicted · none · ref 34 · internal anchor

    RouteFormer is a transformer-RL hybrid for single-agent graph routing that reports 10% and 7% shorter distances than Concorde and LKH-3 on mission-like graphs by incorporating constraints the solvers ignore.

  • ARMATA: Auto-Regressive Multi-Agent Task Assignment cs.MA · 2026-05-05 · unverdicted · none · ref 47

    ARMATA is a new end-to-end autoregressive model with multi-stage decoding that unifies allocation and routing for multi-agent systems and reports up to 20% better solutions than OR-Tools, CPLEX, and LKH-3 in seconds instead of hours.