Startup Requests from YC: Machine Learning in Robotics

Everyone interested in the technological sector knows about Y Combinator. In a nutshell, it represents a series of supportive programs for startups that target specific technological sectors. That’s why it is believed that YC requests for startups represent the most relevant trends in the sphere of modern technologies. So, if you represent a startup, you definitely want to be in one of those niches, and if you represent an enterprise, it might be useful to explore solutions from those niches.

In the series of articles, we are going to review each popular niche from the YC 2024 requests for startups one by one. Seems interesting? Like this post, and let’s get down to exploring the technology of machine learning in robotics.

Applying Machine Learning to Robotics

The YC team believes that applying machine learning to robotics in 2024 has the potential to outperform the achievements of AI-based solutions in 2024. In actual fact, they prove this not only in words but in real actions; One of YC’s founders is known for building the first dynamically balancing bipedal robot.

The interest in robotics is not a new trend. For many years, mass media and pop culture have been promoting the idea that robotics will shape our future. Many experts considered such ideas to be fantasies because robotics couldn’t be called a budget-friendly technology. Nevertheless, thanks to applying machine learning, the fantastic future has become a step closer to our reality. The technology of machine learning added to robotics an important part that has been missing for decades, and that is the reason why YC is very eager to support such startups.

Quick Introduction to Machine Learning Technology

The convergence of machine learning and robotics represents a revolutionary synergy that is reshaping the landscape of automation and intelligent decision-making. This powerful amalgamation equips robots with the capability to learn autonomously, adapt to dynamic environments, and continuously refine their performance through data-driven insights. Unlike conventional rule-based programming, which relies on predefined instructions, the infusion of machine learning into robotics allows these systems to analyse patterns, make predictions, and evolve their actions over time.

This transformative integration opens new frontiers for robots, enabling them to engage in sophisticated tasks such as object recognition, navigation, and complex decision-making. The essence of machine learning in robotics lies in its ability to empower machines with a form of artificial intelligence that goes beyond static programming. AI enables robots to leverage data, recognise trends, and dynamically adjust their behaviour in response to real-world stimuli.

The impact of this union extends across diverse domains, from manufacturing floors optimising production processes to healthcare settings where robots assist in intricate surgeries. The promise of machine learning in robotics lies not just in the amplification of efficiency and versatility but also in propelling these systems toward true autonomy. Autonomous robots can intelligently respond to unforeseen challenges, adapt to varying conditions, and learn from their experiences, mirroring a level of adaptability previously reserved for humans.

Most Interesting Use Cases

The statement that the full potential of applying machine learning to robotics has not been fully discovered yet is proved by the limited number of technology modern applications. Currently, we can focus on the following successful uses of machine learning principles in robotic solutions:

• Assistive Robots: The major function of assistive robots is to detect and process information, choosing the most efficient ways to execute certain actions. Naturally, to ensure that such robots will be able to acquire new information, it is necessary to enable it with machine learning capabilities. Such tools can be effectively used in laboratories and hospitals, for example. However, currently, they seem too expensive to be applied in such budget-limited niches, so this challenge must be overcome.
• Computer Vision: Currently, this case is probably one of the most well-developed examples of successfully applying machine learning to robotics. The merit of such an advancement lies in the execution principle. Computer-vision-based modules process every particle of images they receive and automatically reduce manual effort. This machine-learning technology has already made a great contribution to the development of the automotive industry. We have all heard about driving assistance tools from Tesla and Waymo that maximise the use of machine learning in vehicles. Thanks to this technology and additional hardware equipment, special robots analyse 360-degree views and spot every movement around the vehicle, helping drivers avoid obstacles and see all pedestrians and other vehicles.
• Imitation Learning: In the previous case, robots train their vision based on processed pictures. The case of imitation learning is quite similar, the only difference is that the robots learn from movements, not from visuals. This technology has been around since the introduction of humanoid robots in 1999. Enhanced with the capabilities of machine learning, imitation-trained robots have become an integral part of the manufacturing and construction processes. Currently, they are also being actively applied in the military and security sectors.

As you see, the application of machine learning to robotics has already boosted the efficiency of many processes but its full capabilities are yet to be discovered throughout the following years.

Here are some good examples of how several startups have benefited from these and similar machine-learning solutions:

• GrapeData: The company applied the UI capabilities to boost their interaction with customers through the app. Machine learning helped not only automate notifications but also ensured more sufficient payments.
• Noty.ai: The tool was integrated with such industry giants as Google Meet and Zoom, while also transforming the subscription and payment system.
• Picup Media: In this case, the technical capabilities of machine-learning solutions was used to boost the online visibility of the brand, while getting a lucrative advantage over its competitors.

The Future of the Technology

• More Autonomous to Robots: The task of machine learning technologies for the upcoming future is to make robots more autonomous. AI technology has the full potential to transform pre-programmed and human-managed robots into fully autonomous devices that perform a wide set of functions just like real humans.
• Better Humanoid Robots: The current achievements in the niche of humanoid robotics are pretty limited. In the future, machine learning can contribute to improving the efficiency of robots in industries that require interaction with humans, like the service sector.

• Enhancement of Software Robots: Software robots aren’t the metal tools we can imagine when thinking of robotics. In reality, machine learning has already contributed to the enhancement of software robots. This is clearly visible from the input of generative AI into support chatbots. However, we believe that in the future, thanks to machine learning, software robots will be able to process more complex requests than they do today, providing smooth and efficient functioning of various software solutions.
• Augmenting Robots: The enhancement of augmenting robots has been a huge dream for enthusiasts in the medical industry. Thanks to machine learning, future prostheses will be able to respond to owners’ requests more smoothly and efficiently. Applying machine learning to the prosthetic sector will provide humans who suffer from missing limbs with more specific abilities.
• Robots with AR and VR: Combined with artificial intelligence and machine learning, these technologies have the capability to boost the adaptability of future robots. They will be able not only to efficiently perceive and process our reality but also create their own environments, broadening the scope of their application.

The Bottom Line

As we navigate this new era of rapid technological advancement, the incorporation of machine learning into robotics signifies a paradigm shift. It pushes the boundaries of what is achievable in the realms of automation and artificial intelligence. The symbiotic relationship between machine learning and robotics is not merely about creating more capable machines and enhancing IoT; it is about ushering in a future where machines evolve, learn, and collaborate seamlessly with human counterparts. This convergence marks a pivotal moment in the trajectory of technological innovation, promising a future where robots are not just programmed tools but intelligent, adaptive companions in our quest for progress and efficiency.