Empowering Robotics with Axelera AI: Intelligent Solutions for Advanced Automation
The collaboration between AI and robotics enables robots to implement tasks more effectively and handle complications previously beyond their capacities.
25 Jun, 2025. 10 minutes read
Credit: Agility Robotics
The global vision for developing robotic technologies demonstrates the importance of integrating artificial intelligence (AI) into robots. The National Robotics Initiative (NRI) underscores the transformative role of AI in its 2024 report, "A Roadmap for US Robotics: Robotics for a Better Tomorrow," positioning it as a cornerstone of robotics advancements[1].
The collaboration between AI and robotics enables robots to implement tasks more effectively and handle complications previously beyond their capacities. Autonomy in robots is a key aspect of human-robot interaction (HRI), as it determines how much a robot can operate without human intervention[2]. Advanced AI technologies, including machine learning, computer vision, and natural language processing (NLP), allow robots to comprehend their surroundings and communicate efficiently with humans.
The AI robotics market size is expected to show an annual growth rate of 23.37% over the next five years, resulting in a market volume of US$64.35bn by 2030[3]. This shows an increasingly high demand for AI-powered robots despite challenges like technological integration, high development costs, and resource-heavy AI models.
To address these challenges, Axelera AI empowers robots with real-time, AI-driven insights at the edge. With its secure integration of high performance with low power consumption, Metis AI Processing Unit (AIPU) offers vision AI performance that other accelerators cannot achieve. In robotics, it enables advanced functionalities like object recognition, gesture control, and AI-driven decision-making and autonomy. Its state-of-the-art inference acceleration can help robots unlock unprecedented levels of AI-driven creativity and productivity.
Autonomous Navigation
While traditional robots can effectively navigate with controlled programs, they face significant challenges, such as lack of direction, redundant nodes, and extended computation time in unpredictable or dynamic environments [4]. Advanced Machine Learning (ML) algorithms empower robots by assessing and interpreting large volumes of data in real time, allowing them to make informed decisions when tackling challenges related to environmental complexity and dynamic obstacles.
Axelera AI provides an extensive range of solutions for autonomous navigation with its computer vision and diverse AI applications through innovative hardware and software platforms. With its ability to implement advanced AI models, Axelera AI ensures that navigation processes are both automated and highly accurate. The Metis AIPU empowers robots with complex on-device AI processing algorithms, including high-performance and adaptive features, facilitating data collection, learning, and adaptations for autonomous navigation.
Perception-Driven Navigation and Object Interaction
Robots operating in dynamic environments must perceive, interpret, and react to constantly changing surroundings. Traditional systems, reliant on pre-programmed paths, often fall short when unexpected obstacles, like a moving worker or a misplaced box, enter their way. To navigate and operate safely and efficiently, robots need real-time visual intelligence.
Axelera AI’s Metis AIPU enables advanced perception at the edge by processing high-resolution video streams and running complex AI models directly on-device. This allows robots to analyze their surroundings with low latency, identifying objects and adapting their movements accordingly.
For example, an autonomous mobile robot (AMR) in a warehouse equipped with Axelera AI can detect forklifts or goods in its path and dynamically replan its route to avoid them without cloud dependency or delay. Similarly, in a manufacturing setting, a robotic arm can identify and differentiate between parts on a conveyor belt in real time, ensuring accurate sorting and placement during assembly.
By fusing object recognition with adaptive path planning, Axelera AI empowers robots to make informed decisions based on real-world input, enhancing autonomy and productivity in environments where milliseconds and precision matter.
Human-Robot Interaction (HRI)
Collaborative robots (cobots) face significant challenges due to the variability of the dynamic environment. These robots must recognize human presence, adapt to changing environments, and ensure safety through collision avoidance mechanisms. Trust is critical in HRI, especially when robots have to make autonomous decisions. Consistent robot behavior, transparent decision-making processes, and safety assurances can build trust [5].
Advanced technological solutions and robust operational strategies can address the major issues of human-robot collaboration (HRC) in a dynamic environment that arise from the inherent complexity and unpredictability of settings. With advancements in AI and ML, cobots have overcome the limitations of traditional robots, enabling safe and seamless human-robot interaction. As a result, robotics is moving into areas where AI must be robust enough to anticipate and handle various situations [6]. The need to develop a more intuitive, hands-free, engaging way of interacting with humans drives the convergence of AI and cobots.
Gesture recognition, for instance, allows robots to understand complex gestures better, adapt to individual user behaviors, and deliver real-time responses. This technology represents a sophisticated intersection of AI, computer vision, and ML algorithms, such as convolutional neural networks (CNNs), deep learning models, and pattern recognition techniques.
However, issues like data privacy and bias in HRI persist as we aim for reliable, secure, and fair interactions. Axelera AI addresses these issues with its latest advancements and future directions in AI-driven robotics, highlighting their potential to revolutionize HRI/HRC in diverse domains. It can leverage robots to sense their surroundings, understand human intentions, and respond safely to ensure they can collaborate effectively without compromising safety. For example, in a small manufacturing workshop, a cobot equipped with Axelera AI can assist workers by holding parts in place during assembly. The cobot adjusts its actions based on the worker’s movements, ensuring smooth and safe collaboration.
Voice and Gesture Recognition
NLP has significantly improved the accuracy of voice analysis and effectively detects and responds to the user’s audio state. It allows robots to understand and respond to spoken or written human language. This capability makes robot interactions more intuitive, as users can give commands in natural language rather than through complex programming interfaces. Deep learning models enable emotion patterns from upper body movements to facial expressions to be effectively recognized, providing an effective means of emotion and gesture recognition.
Axelera AI empowers natural human-robot interaction through voice commands and gesture recognition. For instance, an Axelera AI-enabled service robot in a hospital can help with duties like delivering prescription drugs or directing patients to various departments by understanding patient gestures and voice commands from medical personnel.
Emotion Recognition and Adaptive Behavior
AI-enabled emotion recognition technology extensively determines an individual’s emotional state by integrating multiple data sources, including speech, text, facial expressions, and physiological signals, using a variety of algorithms and models, including support vector machines, neural networks, and deep learning, to provide high accuracy.
The board’s AI capabilities allow robots to detect and respond to human emotions. For instance, a companion robot in an elder care facility could use Axelera AI to detect when a resident feels sad or anxious. The robot adapts its behavior by playing soothing music or engaging the resident in conversation, providing emotional support and companionship.
Industrial Automation
Traditional industrial robots have struggled to meet the evolving demands of automation due to their rigid programming and limited adaptability. To overcome these limitations, AI-driven robots gained traction in automation, offering enhanced flexibility, adaptability, and dynamic interaction. For smarter and more sustainable production management, the convergence of AI in robotics is shaping the future of industrial automation by reinforcing predictive maintenance (PdM), quality control, and optimization of production systems. PdM AI tools can help companies boost their labor productivity by 5% to 20%, according to a 2022 Deloitte study [7].
Robots can execute automated maintenance and cleaning in hostile environments. For example, they can clean a tanker ship’s bottom while it is in port without needing a diver to do it, which is unsafe and challenging. Instead of sending divers to the oil rigs in risky locations, robots equipped with cameras perform quality controls on support pillars without the fear of marine life harm.
With Metis, Axelera AI helps adopt and accelerate edge AI in industrial automation for product assembly, defect detection, quality control, and workplace safety inspection. It can handle the challenges in AI hardware, including industrial-grade reliability, low power consumption for extended temperature range, fanless design, low latency, and high throughput for industrial edge applications. Metis provides industrial equipment manufacturers with vision AI performance that other accelerators cannot achieve.
Precision Control and Process Optimization
Manual quality control practices are time-consuming and prone to human error. These are the significant obstacles for precise control and process optimization. AI tools can address these challenges by improving product quality and consistency, minimizing defect rates, and reducing production costs.
Axelera AI enhances the precision and efficiency of industrial robots by providing real-time data analysis and control. The board can optimize welding, painting, or assembling processes, ensuring consistency and quality. For example, in an automotive assembly line, Axelera AI can power robotic arms that perform welding operations with high precision, ensuring consistent quality and reducing waste due to defects.
Predictive Maintenance and Self-Repair
AI algorithms enable predictive maintenance using sophisticated analytics and ML to forecast equipment malfunctions preemptively. They analyze real-time data on equipment health, uncover relevant patterns, and predict failures to stay ahead of outages.
Axelera AI can monitor the health of industrial robots and predict maintenance needs before failures occur. It can also assist in diagnosing issues and guiding robots through self-repair processes, minimizing downtime. For example, a factory using Axelera AI can monitor its fleet of robotic arms and predict when components like gears or motors are likely to fail. The system schedules maintenance proactively, reducing the risk of unexpected breakdowns and improving overall operational efficiency.
Healthcare Robotics
AI-powered robotics is on the verge of transforming healthcare, offering unmatched precision and consistency that exceed human capabilities. In neurosurgery and ophthalmic surgery, submillimeter accuracy enhances precision, leading to faster recovery times and improved patient outcomes. From surgical procedures to medication administration, AI-driven systems are poised to redefine medical excellence. AI helps surgical robots with intelligence and cognitive operational performance in decision-making, problem-solving, voice recognition, and more. It can simplify the medium of interaction between surgical robots and surgeons. This delivers sustainable, equitable, and high-quality healthcare [8].
With its medical segmentation, Axelera AI brings high-speed, real-time AI processing to precision healthcare. Its Metis AIPU can analyze large-scale datasets on-device, reducing latency and eliminating reliance on cloud processing. By accelerating AI-driven data analysis and treatment simulations, Axelera AI enables healthcare workers to make faster, data-informed decisions, all while keeping the data local and helping protect patient privacy.
Surgical Assistance Robots
AI and surgical robots help assess scans and surgeries and improve instrument positioning. As a result, hospitals can improve patient outcomes, enhance operational efficiency, and save costs. Axelera AI can empower robots in minimally invasive surgeries, providing the accuracy and control required for sophisticated procedures. In a surgical suite, for instance, a robot powered by Axelera AI can assist the surgeon in performing delicate spinal surgery, ensuring steady and accurate movements, reducing the risk of complications, and speeding up recovery times for the patient.
Rehabilitation and Physical Therapy Robots
Physical rehabilitation therapy is one of the more efficient approaches to enhancing patients’ functional abilities. By analyzing vast datasets, AI algorithms identify patterns and predict individual treatment responses in tailoring therapies for better patient outcomes.
Axelera AI in robots designed for rehabilitation helps tailor therapy sessions to individual patients by monitoring their progress and adapting exercises accordingly. For example, integrating Axelera AI into a rehabilitation robot can help a patient recover from a stroke. The robot monitors the patient’s movements during exercises, providing real-time feedback and adjusting the difficulty level to match the patient’s progress, enhancing the effectiveness of the therapy.
Agricultural Robotics
The demand for more intelligent, efficient, and sustainable agricultural techniques is increasing as they save time and energy and create unprecedented, supportive farming experiences. With the global shortage of laborers and arable land, integrating AI into robots is crucial for the agricultural industry to achieve higher productivity.
This is where Axelera AI can be a game-changer for agricultural robotics, offering innovative solutions that enhance farming experiences and operations. From enabling precision agriculture to autonomous drones for crop monitoring, Axelera AI empowers robots to meet farmers’ demands. It allows a smooth farming experience with intelligent tracking, customized farming, and improved productivity.
Precision Agriculture
AI-powered drones with high-resolution images and multispectral data provide enough information to detect subtle changes in crop health and accurately diagnose potential issues. This precision is essential in assessing soil health, managing resources, and spotting pests and diseases early.
Axelera AI can allow agricultural robots to perform tasks like planting, weeding, and harvesting with precision. Its AI capabilities enable robots to analyze soil conditions, monitor crop health, and optimize farming practices. In a smart farm, for instance, a robot equipped with Axelera AI can autonomously navigate fields, identifying and removing weeds while precisely applying fertilizers and pesticides only where needed, reducing waste and improving crop yields.
Autonomous Drones for Crop Monitoring
The development of AI-driven autonomous drone technology represents a significant shift in crop monitoring capabilities with valuable insights for optimizing resource allocation. AI-powered drones increase the effectiveness of crop diseases and pests in inhospitable regions where human navigation may be difficult.
The Metis AIPU enables drones to patrol extensive perimeters autonomously. It is equipped with cameras and sensors capable of providing better data collection and analysis for farmers to make more informed decisions in managing their farms. This advanced integration of AI allows for precise and efficient crop monitoring, ensuring swift action when potential issues are identified.
For instance, over a vineyard, drones powered by Metis can continuously capture images and analyze them in real time to detect signs of disease in the grapevines. These drones can play a critical role in providing instant alerts and recommendations for targeted treatments, helping to preserve the crop.
Conclusion
AI-powered robots possess intelligent abilities and can execute with minimum human intervention, bringing flexibility and learning capabilities to previously rigid applications. The convergence of AI and robotics has led to smarter robots, bridging the gap with the physical world. AI helps augment the robots’ abilities to manage complicated tasks and replicate advanced human capabilities. AI algorithms empower robots to learn, adapt, and make intelligent decisions, advancing adaptability and efficiency in manufacturing.
The seamless integration of AI and robotics offers unprecedented approaches to human-robot collaboration. Axelera AI is central to this development, offering robust AI-driven solutions that enhance automation and improve decision-making. From autonomous navigation to human-robot interaction and industrial automation, Axelera AI empowers robots to perform complex tasks with precision and efficiency. This is why integrating AI technologies like Axelera AI is crucial in unlocking the full potential of robots across various industries.
References
[1] A Roadmap for US Robotics: Robotics for a Better Tomorrow (2024). Retrieved from: https://hichristensen.com/pdf/roadmap-2024.pdf
[2] Khedr, M. et al. (2024). An overview of cobots for advanced manufacturing: Human-robot interactions and research trends. MATEC Web of Conferences, 401, 12005. DOI:10.1051/matecconf/202440112005.
[3] AI Robotics - Worldwide (2024). Statista. Retrieved from: https://www.statista.com/outlook/tmo/artificial-intelligence/ai-robotics/worldwide
[4] Q. Du, F. Du, J. Cheng, L. Qi, P. Ji, and B. Huang, “Autonomous Path Planning for Mobile Robots in Multi Obstacle Environments,” Proceedings - 2023 6th International Conference on Computer Network, Electronic and Automation, ICCNEA 2023, pp. 286–289, 2023, DOI: 10.1109/ICCNEA60107.2023.00068.
[5] Chang, S. N. & Lyons, J. B. (Eds.). (2020). Trust in Human-Robot Interaction (1st ed.). Academic Press. Elsevier.
https://shop.elsevier.com/books/trust-in-human-robot-interaction/nam/978-0-12-819472-0
[6] Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Massachusetts.
[7] S. Patil et al. (2020) Predictive maintenance: Deloitte’s approach. Deloitte. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-predictive-maintenance.pdf
[8] Siala, H., & Wang, Y. (2022). SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Social Science & Medicine, 296, 114782. DOI: 10.1016/j.socscimed.2022.114782