Learning Representations for Autonomous Mobile Robotics to Enable Complex Tasks

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We have seen dramatic investments in mobile robotics in recent years, and this is widely considered to be a field of great economic and social promise. The advances of general artificial intelligence (AI), and specifically machine learning (ML) have already yielded significant practical results that are impacting daily life, such as automated face recognition, language translation, and targeted marketing to name a few. However, endowing robots with such capabilities and deploying them in the real world is proving more challenging. But the opportunities for societal impact are significant. Mobile robotics is poised to transform society in the next 20 years in a similar way that industry automation has already in the last 20 years. We have seen limited examples of mobile robotics penetrating the consumer market, most notably applications such as vacuum cleaners and lawn mowers, but massive markets remain open, such as personal transportation, transportation of goods, home assistance and elder care to name a few. One of the core capabilities for any autonomous mobile robot is to be able to perceive the world. This requires amalgamating the stream of sensor data that it is collecting into one coherent and consistent representation. This representation should be rich enough to support the types of tasks that the robot is trying to accomplish. Traditionally, these representations have included little more than geometrical information about the world, and consequently the complexity of the tasks that a robot is capable of achieving have been quite limited. The overall objective of this research program is to develop novel representations that will enable robots to achieve more complex tasks. Recent advances in AI and ML have shown incredible promise. However, the robotics use-case has unique requirements, such as: real-time operation, robustness to incorrect data and failures, scalability, and the ability to understand why a robot took a certain action for safety reasons. Specifically, in this work I will investigate new representations that contain higher level semantic information and statistics about the temporal variability of the world. I will also pay special attention the scalability and real-time requirements of these algorithms as they relate to the available onboard resources. This will be achieved by leveraging deep learning to provide the data-preprocessing in the form of object detections and semantic segmentations. Additionally, the allocation of resources such as computation, memory, and bandwidth, must be done in a way that maximizes the probability of successfully achieving the stated task Robotics has the potential to impact society in a profound way. For this to become a reality requires that robots are safe, robust, and reliable. Achieving these goals requires that robots have a more profound understanding of their surroundings, and that their algorithms are able to scale well.