具身智能自主无人系统技术

孙长银; 袁心; 王远大; 柳文章

doi:10.16383/j.aas.c240456

具身智能自主无人系统技术

doi: 10.16383/j.aas.c240456 cstr: 32138.14.j.aas.c240456

孙长银^{1, 2, 3,},
袁心^3,,
王远大^3,,
柳文章^2,

1.
安徽大学自主无人系统技术教育部工程研究中心合肥 230601
2.
安徽大学安徽省无人系统与智能技术工程研究中心合肥 230601
3.
东南大学自动化学院南京 210096

基金项目: 国家自然科学基金创新研究群体(61921004), 国家自然科学基金重点项目(62236002), 国家自然科学基金(62203113)资助

详细信息

作者简介:
孙长银：安徽大学人工智能学院教授. 1996年获得四川大学应用数学专业学士学位. 分别于2001年, 2004年获得东南大学电子工程专业硕士和博士学位. 主要研究方向为智能控制, 飞行器控制, 模式识别和优化理论. 本文通信作者. E-mail: cysun@seu.edu.cn

袁心：东南大学自动化学院博士后, 2021年获得东南大学控制科学与工程专业博士学位. 主要研究方向是深度强化学习和最优控制. E-mail: xinyuan@seu.edu.cn

王远大：东南大学自动化学院博士后, 2020年获得东南大学控制科学与工程专业博士学位. 主要研究方向是深度强化学习和机器人系统控制. E-mail: wangyd@seu.edu.cn

柳文章：安徽大学人工智能学院讲师. 2022年获得东南大学控制科学与工程博士学位. 主要研究方向包括深度强化学习, 多智能体强化学习, 迁移强化学习, 机器人等. E-mail: wzliu@ahu.edu.cn

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出版历程
- 录用日期: 2024-09-27
- 网络出版日期: 2024-10-23

Embodied Intelligence Autonomous Unmanned Systems Technology

SUN Chang-Yin^{1, 2, 3
,},
YUAN Xin^3
,,
WANG Yuan-Da^3
,,
LIU Wen-Zhang^2
,

1.
Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui University, Hefei 230601
2.
Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Anhui Uni-versity, Hefei 230601
3.
School of Automation, Southeast University, Nanjing 210096

Funds: Supported by Foundation for Innovative Research Groups of National Natural Science Foundation of China (61921004), Key Projects of National Natural Science Foundation of China (62236002), and National Natural Science Foundation of China (62236002, 62203113)

More Information

Author Bio:
SUN Chang-Yin　Professor at the School of Artificial Intelligence, Anhui University. He received his bachelor degree in applied mathematics from Sichuan University in 1996, and his master and Ph.D. degrees in electrical engineering from Southeast University in 2001 and 2004, respectively. His research interest covers intelligent control, flight control, pattern recognition, and optimal theory. Corresponding author of this paper

Yuan Xin,　a postdoctoral researcher at the School of Automation, Southeast University, received his Ph.D. in Control Science and Engineering from Southeast University in 2020. His main research areas are deep reinforcement learning and optimal control

Wang Yuan-Da　a postdoctoral researcher at the School of Automation, Southeast University, received his Ph.D. in Control Science and Engineering from Southeast University in 2020. His main research areas are deep reinforcement learning and robotic system control

Liu Wen-Zhang　Lecturer at the School of Artificial Intelligence, Anhui University. He received his Ph.D. degree in engineering from the School of Automation, Southeast University, Nanjing, China, in 2022. His current research interests include deep reinforcement learning, multi-agent reinforcement learning, transfer reinforcement learning, and robotics, etc

摘要

摘要: 自主无人系统是一类具有自主感知和决策能力的智能系统, 在国防安全、航空航天、高性能机器人等方面有着广泛的应用. 近年来, 基于Transformer架构的各类大模型快速革新, 极大地推动了自主无人系统的发展. 目前, 自主无人系统正迎来一场以“具身智能”为核心的新一代技术革命. 大模型需要借助无人系统的物理实体来实现“具身化”, 无人系统可以利用大模型技术来实现“智能化”. 本文阐述了具身智能自主无人系统的发展现状, 详细探讨了包含大模型驱动的多模态感知、面向具身任务的推理与决策、基于动态交互的机器人学习与控制、三维场景具身模拟器等具身智能领域的关键技术. 最后, 指出了目前具身智能无人系统所面临的挑战, 并展望了未来的研究方向.
- 自主无人系统 /
- 具身智能 /
- 大语言模型 /
- 人工智能
Abstract: Autonomous unmanned systems are intelligent systems with autonomous perception and decision-making capabilities, widely applied in areas such as defense security, aerospace, and high-performance robotics. In recent years, the rapid advancements of various large models based on the Transformer architecture have significantly accelerated the development of autonomous unmanned systems. Currently, these systems are undergoing a new technological revolution centered on “embodied intelligence.” Large models require the physical embodiment of unmanned systems to achieve “embodiment,” while unmanned systems can leverage large model technologies to achieve “intelligence.” This paper outlines the current state of development in embodied intelligent autonomous unmanned systems and provides a detailed discussion of key technologies in the field of embodied intelligence, including large-model-driven multimodal perception, reasoning and decision-making for embodied tasks, robot learning and control based on human-computer interaction, and 3D embodied simulators. Finally, the paper identifies existing challenges in autonomous unmanned systems and explores future research directions.
- Autonomous unmanned systems /
- embodied intelligence /
- large language models /
- artificial intelligence

HTML全文

图 1 自主无人系统体系架构发展趋势

Fig. 1 Architecture development trend of autonomous unmanned systems

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图 2 PaLM-E完成长程任务

Fig. 2 The PaLM-E completes long range tasks

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图 4 各类人形机器人

Fig. 4 Various humanoid robots

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图 3 具身智能无人系统关键技术结构示意图

Fig. 3 Schematic diagram of key technical structure of autonomous intelligent unmanned system

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图 5 具身智能自主无人系统框架示意图及典型应用

Fig. 5 Framework diagram and typical application of embodied intelligent autonomous unmanned system

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图 6 具身智能未来研究方向

Fig. 6 Future research direction of embodied intelligence

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表 1 具身智能模型架构

Table 1 Embodied intelligence model architecture

名称	模型参数	响应频率	模型架构说明
SayCan^[75]	—	—	SayCan利用价值函数表示各个技能的可行性, 并由语言模型进行技能评分, 能够兼顾任务需求和机器人技能的可行性
RT-1^[31]	350万	3 Hz	RT-1采用13万条机器人演示数据的数据集完成模仿学习训练, 能以97%的成功率执行超过700个语音指令任务
RoboCat^[108]	12亿	10 ~ 20 Hz	RoboCat构建了基于目标图像的可迁移机器人操纵框架, 能够实现多个操纵任务的零样本迁移
PaLM-E^[32]	5620亿	5 ~ 6 Hz	PaLM-E构建了当时最大的具身多模态大模型, 将机器人传感器模态融入语言模型, 建立了端到端的训练框架
RT-2^[33]	550亿	1 ~ 3 Hz	RT-2首次构建了视觉-语言-动作的模型, 在多个具身任务上实现了多阶段的语义推理
VoxPoser^[52]	—	—	VoxPoser利用语言模型生成关于当前环境的价值地图, 并基于价值地图进行动作轨迹规划, 实现了高自由度的环境交互
RT-2-X^[105]	550亿	1 ~ 3 Hz	RT-2-X构建了提供了标准化数据格式、交互环境和模型的数据集, 包含展示了527种技能和16万个任务

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