The past decade has witnessed remarkable advancements in robotics and AI technologies, ushering in the era of autonomous machines. In this new age, service robots, autonomous drones, delivery robots, self-driving vehicles and other autonomous machines are poised to replace humans in providing various services.5 While the rise of autonomous machines promises to revolutionize our economy, the reality has fallen short of expectations despite over a decade of intensive R&D investments. The current development paradigm, dubbed Autonomy 1.0, scales mainly with the size of engineering team rather than with the amount of relevant data or computational resources. This limitation prevents the autonomy industry from fully leveraging economies of scale, particularly the exponentially decreasing cost of computing power and the explosion of available data.

With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the technical challenges and economic impact of the digital economy, we argue that scalability is both highly necessary from a technical perspective and significantly advantageous from an economic perspective, thus is the key for the autonomy industry to achieve its full potential. Nonetheless, the current development paradigm, dubbed Autonomy 1.0, scales with the number of engineers, instead of with the amount of data or compute resources, hence preventing the autonomy industry to fully benefit from the economies of scale, especially the exponentially cheapening compute cost and the explosion of available data. We further analyze the key scalability blockers and explain how a new development paradigm, dubbed Autonomy 2.0, can address these problems to greatly boost the autonomy industry.

At Woven Planet Level 5, we're using machine learning (ML) to build an autonomous driving system that improves as it observes more human driving. This is based on our Autonomy 2.0 approach, which leverages machine learning and data to solve the complex task of driving safely. This is unlike traditional systems, where engineers hand-design rules for every possible driving event. Last year, we took a critical step in delivering on Autonomy 2.0 by using an ML model to power our motion planner, the core decision-making module of our self-driving system. We saw the ML Planner's performance improve as we trained it on more human driving data.

Despite the numerous successes of machine learning over the past decade (image recognition, decision-making, NLP, image synthesis), self-driving technology has not yet followed the same trend. In this paper, we study the history, composition, and development bottlenecks of the modern self-driving stack. We argue that the slow progress is caused by approaches that require too much hand-engineering, an over-reliance on road testing, and high fleet deployment costs. We observe that the classical stack has several bottlenecks that preclude the necessary scale needed to capture the long tail of rare events. To resolve these problems, we outline the principles of Autonomy 2.0, an ML-first approach to self-driving, as a viable alternative to the currently adopted state-of-the-art. This approach is based on (i) a fully differentiable AV stack trainable from human demonstrations, (ii) closed-loop data-driven reactive simulation, and (iii) large-scale, low-cost data collections as critical solutions towards scalability issues. We outline the general architecture, survey promising works in this direction and propose key challenges to be addressed by the community in the future.

A decade ago, industry stakeholders thought fully self-driving vehicles (SDVs) would become a reality in five years. It's 2021 already, and there are still no signs of autonomous vehicles at a scale many experts had anticipated. Five years ago, GM spent $581 million to acquire Cruise Automation. In 2017, GM chief Mary Barra wrote, "We expect to be the first high-volume auto manufacturer to build'fully-autonomous vehicles' in a mass-production assembly plant." At the time, GM president Daniel Ammann, said, "When you are working on the large-scale deployment of mission-critical safety systems, the mindset of'move fast and break things certainly does not cut it."