In the morning, we rise and skillfully guide the toothbrush to cleanse our teeth. Following that, we initiate our day by taking measured sips of coffee, deftly managing the cup, and savouring its contents. These seemingly routine actions, facilitated by our hands and eyes, often go unnoticed in their intricate precision. However, the intricate journey of visual perception's evolution and the object-grasping capability spans millions of years. This journey entails a complex fusion of anatomical adaptations, cognitive advancement, and cultural ingenuity. These abilities have played a critical role in shaping the evolution of early primates into modern humans and have enabled us to interact with and understand the world around us in unique ways. Replicating this ability on a robotic manipulator is a billion-dollar market in the making. 

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Such robots with visual perception and grasping have applications in healthcare, defense, space, the food industry, agriculture, meat production, nuclear science, etc. Those who infuse artificial intelligence into robots are known as roboticists and Karthikeyan Yuvaraj is among some of the most prominent names in this realm. Yuvaraj, a robotics researcher and engineer with nearly 10 years of experience, has significant contribution to the development of robots for applications in disaster recovery situations and the logistics/supply chain space. 

Fukushima nuclear accident - that took place in March 2011 at the Fukushima Daiichi Nuclear Power Plant in Okuma, Fukushima, Japan -  served as the catalyst for the creation of the DARPA Robotics Challenge (DRC) by the United States Defense Advanced Research Projects Agency (DARPA). The DRC aimed to cultivate robotic systems capable of functioning as disaster response agents. The challenges in the competition spanned tasks like turning valves, climbing ladders, driving utility vehicles, and manipulating drills. 

In warehouse settings like those of FedEx and UPS, the orderly arrangement of goods often faces disruption. Tasks encompass actions such as selecting and positioning packets on conveyors, retrieving and scanning packets, extracting single items from stacks, unloading hefty cargo from trucks, and repositioning cargo-laden pallets from one location to another. To keep goods efficiently traversing the country, these activities demand rigorous human labour throughout the clock cycle. Notably, unloading cargo from trucks poses a particularly strenuous and labor-intensive challenge, exerting considerable strain on the human back.

Different use-case scenarios of robotic manipulation

Autonomous Navigation: From Controlled to Smart Mobility

Traditional robots were programmed with precise paths and movements, limiting their flexibility in dynamic environments. The incorporation of AI has transformed robotics into intelligent entities capable of navigating complex terrains autonomously. This trend is evident in the development of autonomous drones, self-driving cars, etc. AI algorithms process real-time data from sensors and cameras, enabling robots to make split-second decisions, avoid collisions, and reach their destinations safely.

Cognitive Abilities: Infusing Robots with AI Minds

Advancements in AI have unlocked the potential for robots to possess cognitive capabilities akin to human intelligence. Cognitive robots can perceive their surroundings, understand natural language, and even exhibit problem-solving skills. This trend is revolutionizing sectors like customer service, where chatbots equipped with natural language processing can engage in meaningful conversations with customers. In healthcare, robots are being trained to assist surgeons by anticipating their needs during surgeries. These developments pave the way for robots to not only perform physical tasks but also engage in complex decision-making processes.

Edge AI in Robotics: Processing Power at the Source

When we have an autonomous car driving at 60 to 70 mph and when a pedestrian walks across the street, there is no luxury of time to process the sensory information in the cloud and then communicate the action. The autonomous car needs to make a split-second decision to plan a collision-free path or come to a stop. Edge computing, which involves processing data closer to the source rather than sending it to a centralized server, is transforming robotics. By incorporating AI processing at the edge, robots can analyze data on-site, leading to quicker and more efficient decision-making. This also reduces the reliance on constant high-speed internet connections, making robots more adaptable to various environments.

Automation in the Agricultural space

The process of cultivating a specific crop and delivering it to consumers is a highly demanding undertaking. Farmers dedicate extensive hours to preparing the land, planting seeds, overseeing crop growth, managing pesticide applications, and executing timely harvesting. The realm of startups and established enterprises is filled with initiatives directed at developing robotic systems capable of overseeing crops and, where necessary, eliminating detrimental pesticides. Mobile robotic manipulation systems have the capacity to traverse a certain fruit farm, accurately assess the ripeness of fruits, and subsequently harvest and place them into containers. Autonomous tractors can plough the land once they have a basic instruction from the operator and also automatically water the plants based on the plant type. These innovative forms of robotics and AI technology are poised to drive the evolution of sustainable farming practices.