Key Takeaways & Executive Findings
- •• Robotic algorithms are evolving from traditional to hierarchical and end-to-end models, posing challenges for balanced system-wide performance. • Algorithm-hardware co-design is essential to analyze computational properties and drive both algorithm optimization and hardware innovation. • Recent works demonstrate co-design across robotic and embodied AI algorithms and computing hardware, achieving high performance and energy efficiency. • Future research must adapt computing platforms to rapid embodied AI evolution and leverage emerging hardware for end-to-end inference improvements.
Abstract
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algorithms and supporting hardware. In recent years, the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models. This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance. Therefore, algorithm-hardware co-design has emerged as the primary methodology, which analyzes algorithm behaviors on hardware to identify common computational properties. These properties can motivate algorithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency. We then reviewed recent works on robotic and embodied AI algorithms and computing hardware to demonstrate this algorithm-hardware co-design methodology. In the end, we discuss future research opportunities by answering two questions: (1) how to adapt the computing platforms to the rapid evolution of embodied AI algorithms, and (2) how to transform the potential of emerging hardware innovations into end-to-end inference improvements.
1. Introduction
Robotics has made remarkable strides over the past few decades, pushing the boundaries of what machines can accomplish autonomously or in collaboration with humans. Various robotic systems have been researched and developed in many domains. For example, in healthcare scenarios, surgical robots like the da Vinci Surgical System perform minimally invasive procedures with precision beyond human capability, improving patient outcomes and redefining standards of care. In manufacturing sectors, collaborative robots, or "cobots", are now common in factories, working alongside human operators to enhance productivity, safety, and flexibility on the production floor.
A typical robotic system comprises a physical entity as its "body" and an intelligent agent as its "brain". The entity serves as the platform for interaction with the physical world and can manifest in various forms, such as manipulator arms, humanoid robots, quadrupeds, or drones, tailored to specific applications. The agent, conversely, comprises three fundamental subsystems: sensory, computing, and actuation. The sensory system employs various sensors (e.g., cameras, LiDAR) to gather data about the robot’s internal state and the external environment. The actuation system, composed of servomotors, drives, and transmissions, enables the robot to act upon the environment.
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YAN Longke, ZHAO Xin, YANG Bohan, WU Yongkun, DAI Guangnan, LI Jiancong, TSUI Chi-Ying, CHENG Kwang-Ting, ZHANG Yihan, TU Fengbin (2025). Robotic computing system and embodied AI evolution: an algorithm-hardware co-design perspective. SinoTechIntel Verified Research. https://doi.org/10.1088/1674-4926/25020034
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Frequently Asked Questions
What is the main focus of the paper 'Robotic computing system and embodied AI evolution'?
The paper focuses on the evolution of robotic computing systems and embodied AI, emphasizing the importance of algorithm-hardware co-design to achieve balanced system-wide performance, high performance, and energy efficiency.
What are the key challenges in robotic computing systems discussed in the paper?
The key challenge is the rapid evolution of robotic algorithms from traditional to hierarchical and end-to-end models, which requires balanced system-wide performance. This necessitates algorithm-hardware co-design to optimize both computational complexity and hardware architecture.
How does algorithm-hardware co-design benefit robotic systems?
Algorithm-hardware co-design analyzes algorithm behaviors on hardware to identify common computational properties, motivating algorithm optimization to reduce complexity and hardware innovation from architecture to circuit, leading to high performance and energy efficiency.
What future research opportunities are highlighted in the paper?
The paper highlights two future research directions: adapting computing platforms to the rapid evolution of embodied AI algorithms, and transforming emerging hardware innovations into end-to-end inference improvements.
What are the key components of a typical robotic system according to the paper?
A typical robotic system comprises a physical entity (body) and an intelligent agent (brain). The agent includes sensory, computing, and actuation subsystems, which gather data, process information, and act on the environment, respectively.
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