# Advancing Behavior Generation in Mobile Robotics through High-Fidelity Procedural Simulations

arXiv: https://arxiv.org/abs/2405.16818 · HTML: https://mlsanity.com/paper/2405.16818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 27 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 10 of 10)

- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models](https://mlsanity.com/paper/2310.18308/) (2023)
- [LLM as A Robotic Brain: Unifying Egocentric Memory and Control](https://mlsanity.com/paper/2304.09349/) (2023)
- [Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning](https://mlsanity.com/paper/2103.04616/) (2021)
- [Simulators for Mobile Social Robots:State-of-the-Art and Challenges](https://mlsanity.com/paper/2202.03582/) (2022)
- [Jubileo: An Open-Source Robot and Framework for Research in Human-Robot Social Interaction](https://mlsanity.com/paper/2209.13509/) (2022)

## Built on by (most cited first, 1 of 1)

- [Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation](https://mlsanity.com/paper/2506.00075/) (2025)

## Abstract (arXiv)

This paper introduces YamaS, a simulator integrating Unity3D Engine with Robotic Operating System for robot navigation research and aims to facilitate the development of both Deep Reinforcement Learning (Deep-RL) and Natural Language Processing (NLP). It supports single and multi-agent configurations with features like procedural environment generation, RGB vision, and dynamic obstacle navigation. Unique to YamaS is its ability to construct single and multi-agent environments, as well as generating agent's behaviour through textual descriptions. The simulator's fidelity is underscored by comparisons with the real-world Yamabiko Beego robot, demonstrating high accuracy in sensor simulations and spatial reasoning. Moreover, YamaS integrates Virtual Reality (VR) to augment Human-Robot Interaction (HRI) studies, providing an immersive platform for developers and researchers. This fusion establishes YamaS as a versatile and valuable tool for the development and testing of autonomous systems, contributing to the fields of robot simulation and AI-driven training methodologies.