# BestMan: A Modular Mobile Manipulator Platform for Embodied AI with Unified Simulation-Hardware APIs

arXiv: https://arxiv.org/abs/2410.13407 · HTML: https://mlsanity.com/paper/2410.13407/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation](https://mlsanity.com/paper/2503.11081/) (2025)
- [GSAM: A Generalizable and Safe Robotic Framework for Articulated Object Manipulation](https://mlsanity.com/paper/2605.30740/) (2026)
- [One-to-Two Acting: A Novel Framework for Single-arm Agent Action Expansion to Dual Arms](https://mlsanity.com/paper/2606.19897/) (2026)

## Abstract (arXiv)

Embodied Artificial Intelligence (Embodied AI) emphasizes agents' ability to perceive, understand, and act in physical environments. Simulation platforms play a crucial role in advancing this field by enabling the validation and optimization of algorithms. However, existing platforms face challenges such as multilevel technical integration complexity, insufficient modularity, interface heterogeneity, and adaptation to diverse hardware. We present BestMan, a simulation platform based on PyBullet, designed to address these issues. BestMan introduces an integrated multilevel skill chain for seamless coordination across perception, planning, and control; a highly modular architecture for flexible algorithm integration; unified interfaces for smooth simulation-to-reality transfer; and a hardware-agnostic approach for adapting to various mobile manipulator configurations. These features collectively simplify development and enhance platform expandability, making BestMan a valuable tool for Embodied AI research.