BestMan统一仿真与硬件接口，让机器人开发更灵活高效。
BestMan: A Modular Mobile Manipulator Platform for Embodied AI with Unified Simulation-Hardware APIs
- 构建多层级技能链，打通感知-规划-控制流程
- 模块化设计支持灵活算法接入，降低集成复杂度
- 硬件无关架构适配多种移动操作机器人
具身人工智能强调智能体在物理环境中感知、理解并行动的能力。仿真平台在推动该领域发展中起关键作用，可验证和优化算法。然而现有平台存在多层技术集成复杂、模块化不足、接口异构以及难以适配多样化硬件等问题。本文提出BestMan，一个基于PyBullet的仿真平台，通过引入跨感知、规划与控制的集成多层级技能链；高度模块化的架构以实现灵活算法集成；统一接口支持从仿真到现实的平滑迁移；以及硬件无关方法，可适应多种移动操作机器人配置。这些特性共同简化开发流程，提升平台可扩展性，为具身人工智能研究提供有力工具。
原文摘要 · Abstract (English)
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.
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