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The lessons we learn from autonomous driving will lead to our robotic future

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Robotics is entering a phase of exponential growth. There are more and more new and diverse applications for robots that are both inspiring and mundane. Just in the context of the response to the COVID-19 pandemic, robots have been deployed in new ways – to disinfect public spaces, handle infectious materials, and provide medical care to patients.

But the horizon of new robotic applications continues to widen, and it is the development of AV (autonomous vehicle) that will further accelerate this growth. Why? Because the challenge that self-driving cars present is the same challenge that acts as a barrier for most other types of robots. The AV industry, with its concentration of talent, infrastructure and capital, is ready to meet this challenge.

The challenge of autonomy

Although the use of robots has become widespread, its applications have remained somewhat limited. For decades, the giant penguins have performed highly scripted tasks and were built for a single purpose, such as spot welding or adding wires to the end of a pipe. They weren’t flexible enough to perform a variety of tasks or respond well in unstructured environments. Even deployed in less structured environments, such as those used in surgical settings or even aerial drones, the robots functioned primarily as a remote-controlled extension of a human actor, with limited autonomy.

VAs, on the other hand, inherently require a great deal of autonomy; there is literally no human being behind the wheel, and the stakes are high. AVs must be able to sense, plan and act in highly dynamic and unstructured environments such as the chaotic streets of San Francisco. They have to respond to humans – other drivers, pedestrians, cyclists, that guy on a motorized skateboard – and make decisions together with them.

Consider one of the most common yet difficult traffic scenarios that humans face on a regular basis: a four-lane stop. Despite the laws that govern how drivers should stop and take turns, the reality is that most of the time people navigate these intersections through non-verbal communication with each other. They make eye contact, nod, greet each other. Without the ability to communicate using these cues, an AV must still decipher the intention of other drivers and communicate his own – for example, drive slowly to convey his intention to cross the intersection – while obeying the rules of the road and by making security critical. the decisions. This choreography cannot be scripted in advance. Audiovisual decision-making must conform to human social expectations in real time based on the current situation and the potential development of all relevant actors in the scene, including herself, for some time in the future .

The crux of the challenge is to make decisions under uncertainty; that is, choose actions based on often imperfect observations and incomplete knowledge of the world. Autonomous robots must observe the current state of the world (imperfect observations), understand how it is likely to evolve (incomplete knowledge), and make decisions about the best course of action to take in each situation. This cognitive ability is also essential for interpersonal interactions because human communications presuppose an ability to understand the motivations of participants and topics of discussion. As the complexity of human-machine interactions increases and automated systems become smarter, we strive to provide computers with comparable communication and decision-making capabilities. This is what moves robots from machines supervised by humans to machines with which humans can collaborate.

Where human-robot collaboration can take us

As robotics has developed as an industry, costs have come down, allowing its adoption in a wide variety of contexts. In some cases the technology is familiar but the application is new. While drones aren’t new, companies that deploy them to inspect power lines or to collect information for insurance claims is. Ditto for the giant penguins now employed as hotel concierges or baristas instead of spot welders.

Commerce has benefited greatly from automation. Material handling in particular is ripe for automation via autoguided vehicles, in large part because it is such a dangerous industry for human workers. Robots equipped with lidar, cameras, and a host of other sensors, such as those that activate AV perception systems, can navigate loading docks and factories quickly and safely while avoiding collisions with the workers. However, these robots still rely on a fairly structured and predictable environment (markers on the ground help them navigate) and lack dynamic responsiveness. In recent years, some have argued that injuries in some distribution centers resulted from robots moving at a faster rate than the humans working alongside them.

Robotics in healthcare environments has also become commonplace. Robot-assisted surgical systems like Intuitive’s Da Vinci are used in 90% of prostatectomies instead of traditional laparoscopic tools. But robots are increasingly valuable not only in the operating room, but in all hospitals and nursing homes, especially in the context of the COVID-19 pandemic. Robots help caregivers lifting patients and performing other tasks as well as to provide social interaction with the elderly. Robotics is also increasingly used with children, not only as trendy tech toys, but also as legitimate STEM educational tools. Research on treatment of autistic children using emotional robots has gained ground in recent years.

Audiovisual development is the key

With more players on the ground and growing adoption, the The more than $ 100 billion global robotics industry has grown by leaps and bounds, and according to IDC is expected to triple by the end of 2021. Much of this can be attributed to driver assistance technologies now common in new vehicles, especially those at the high end of the market. However, companies that develop fully autonomous technology are poised to push the boundaries of robotics in the automotive industry and beyond.

As audiovisual companies rise to the challenge of human-robot collaboration at the level required to bring autonomous vehicles to market, the horizon for leveraging these solutions for other robotic applications is only widening. Like a grandmaster of chess, an AV must consider several possible movements and countermovements for both himself and other participants in the traffic, and then make critical decisions for safety in a noisy and fast-paced environment. . It must take into account the context such as the highway code and local standards; driving in a city like Houston is not the same as sailing in Hong Kong. And a successful VA must communicate their goals and intention to humans in a way that feels natural and intuitive.

Developing the kind of decision-making necessary for successful AVs will unlock complex ‘critical thinking’ for other robotic applications, enabling a greater degree of autonomy and human-robot collaboration in new and familiar use cases. . Physical agents capable of autonomously generating engaging and realistic behavior will lead to safer and more responsive robots. The shift from humans overseeing robots to working with them is the way forward for AVs and the industry as a whole.

Rashed Haq is vice president of robotics at Cruise.

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