The Next Wave in Mobile Robotics: Generative Autonomy
Updated: Oct 10
The field of robotics has always been dynamic, reflecting our continuous quest to push technological boundaries. From the early days of simple, programmable machines to the sophisticated robots we see today, each advancement has been a step toward creating more autonomous and intelligent machines.
Generative AI: The Foundation for Adaptive Automation
Generative AI is a transformative branch of artificial intelligence. Unlike traditional AI models that predict or classify data, generative models produce new content. This could range from synthesizing images that resemble photographs to generating coherent and contextually relevant text.
In the context of mobile robotics, the implications are profound. Once bound by the constraints of their programming, mobile robots will be equipped with the capability to generate responses or actions based on the data they encounter. This is not mere automation; it's creation, adaptation, and evolution. At RGo, we call this concept Generative Autonomy.
Generative Autonomy: The Next Step for Smart Robotics
Generative Autonomy takes the principles of generative AI and applies them to the realm of mobile robotics. It shifts robots from being simple task performers to entities capable of independent thought. Under this framework, robots aren't bound by rigid programming for every situation. They can assess, interpret, and dynamically react to their environment without continuous human intervention.
Take a warehouse as an example. A robot consistently moves from point A to B, transporting items. Suddenly, an obstacle appears in its usual path. The robot intelligently circumvents this barrier and proceeds. However, if the same obstacle persists for days, generative AI intervenes. The robot 'understands' this might be a permanent change and reevaluates its route. A new path is generated, considering everything the robot knows about the warehouse, obstacles and congestion. It modifies the route not only for one specific robot but for the entire fleet, enhancing overall efficiency across the entire warehouse.
Today, when a mobile robot encounters a human or a complex scene of humans and other machines (e.g. forklifts) it slows and often stops completely. In this new paradigm, it will do more than perceive – it will understand the scene, just like humans do. If the human is going left and the forklift is going to the right, it will take measures to assess the safety of moving straight ahead. And since it detects that one of the obstacles is human, it will be able to communicate (lights or audio) to message its intentions and further establish order to the situation. This increases safety and reduces inefficiencies caused by robots moving slowly or tentatively in complex environments.
Imagine the possibilities. Robots will be able to handle changing environments and will adapt automatically. There will be no need to code the robot to every scenario and every path anymore. Shorter development cycles, more robots, faster innovation. It will enable robots to work with us, in our natural environment.
In outdoor scenarios, an autonomous forklift could identify and avoid wet patches after rainfall, ensuring smooth operations and reducing the risk of getting mired. In a retail setting, a robot might detect increased foot traffic during a holiday season. Using its generative AI capabilities, it can adjust its route to what will likely be slower moving product aisles to reduce disruptions. A home assistant robot, upon noticing its owner's altered gait, might deduce a potential injury and modify its pace to offer better support.
Generative Autonomy will also simplify the installation of robots. The robot will learn autonomously. Today every robot installation is a painful process. One has to map the area, fix and clean the map and assign labels on it, and only then design each and every path for the robot. With Generative Autonomy you will just give the robot the current warehouse map, the same map a warehouse employee gets, and the robot will look around and understand it. Just like how humans do it. It will be that simple.
Redefining Perception: The Role of Visual Information in Mobile Robotics
For robots to truly 'understand' their surroundings and interpret these situations, they need advanced perception – really, human-level perception. And visual information is by far the most valuable because it can provide precise position, obstacle, and scene understanding and ways that no other type of sensor can match. Vision-based systems can also leverage color, written text and symbols, texture, and a host of other attributes that we as humans use every day. RGo is rooted in visual perception and pushing its envelopes every day.
AI is, of course, another cornerstone. Combined with today’s powerful and ubiquitous compute platforms, the vast amount of perception data are converted into meaningful actions. Robots can move beyond the mere execution of pre-set tasks. Instead, they adapt and learn from their environment, much like humans draw from experience to make decisions and respond with limited external input. And they must operate efficiently while adhering to budget constraints and ensure that a reasonable effort is made in tagging the training sets. Efficient hardware implementation of transformer neural networks could be key.
But visual perception and AI are still not enough.The process of developing and scaling fleets will need to change. Robots will need to be permitted to make mistakes. While safety remains paramount, missteps are no longer mere errors. Instead, they become invaluable learning opportunities, sharpening the robot's understanding and decision-making. Errors, become rich data points. As they accrue experiences, the robot’s and fleet’s error-handling strategies mature, enabling them to preempt issues, decipher ambiguities, and enhance decision accuracy.
Ushering in the New Era
Generative autonomy is reshaping the robotics landscape. As robots evolve, they're not just executing tasks but adapting to challenges, much like how children learn and develop. This progression hints at a future where robots could assist in more complex roles, from logistics to healthcare to last-mile delivery, opening doors to innovations we've yet to imagine. Tune into this episode of the The Robot Industry Podcast to learn more about how Generative Autonomy is advancing mobile robotics in warehouses.