Summary of Spring 2022 GRASP SFI - Ted Xiao, Robotics at Google, “A Panorama of End to end Robot Learning”

00:00:00 - 00:55:00

In the video, Ted Xiao discusses the work that his team at Google is doing on end-to-end robot learning. He believes that this methodology is biologically plausible and able to rise to the challenge in scenarios where less end-to-end methods might struggle. Additionally, he believes that this type of learning is compelling because it is consistent with human behavior.

• 00:00:00 Ted Xiao, a robotics researcher at Google, discusses the current state of robot learning and discusses why end-to-end learning is a promising approach. He also discusses some of the problems that end-to-end learning could help solve.
• 00:05:00 In 2022, robotics technology is transitioning to a "panorama of end-to-end learning." This is when different parts of the technology, such as learning algorithms, state estimation, and control, are replaced by learning. Similar to earlier transitions in other fields, this transition happened gradually over time, with each stage becoming more reliant on learning. In 2021 and 2022, different options are available for applying learning to robotic systems. One option is to use learning to replace certain components of a classical robotic system, such as localization and state estimation. Another option is to use learning to create a system that can autonomously navigate and complete tasks in an ever-changing environment.
• 00:10:00 Ted Xiao, a robotics expert at Google, discusses the different phases of end-to-end robot learning, which include acquiring knowledge of the environment, adapting and autonomously planning, and operating in an unknown environment. He believes that a learning-based approach is the best way to navigate this challenge, as it does not rely on hard-coded assumptions about the environment.
• 00:15:00 This video introduces Ted Xiao, a robotics researcher at Google, and discusses his work on end-to-end robot learning. Xiao believes that this methodology is biologically plausible and able to rise to the challenge in scenarios where less end-to-end methods might struggle. Additionally, he believes that this type of learning is compelling because it is consistent with human behavior. Finally, Xiao discusses Sutton's bitter lesson about scale and how it applies to robotics.
• 00:20:00 In the video, Ted Xiao discusses the importance of high-capacity, descriptive models and large, diverse data sets for effective robot learning. He also suggests that end-to-end learning, which takes these two factors into account, is a promising direction for robotics research.
• 00:25:00 In this video, Ted Xiao, robotics engineer at Google, discusses some of the challenges and opportunities for reinforcement learning methods in the future. He also discusses some of the work that his team has been doing to address those challenges.
• 00:30:00 The team at Google has been working on a panorama of end-to-end robot learning, with a focus on improving policies robust to simulation and real-world performance. They have also been working on concurrent control to enable robots to still perform actions while in motion.
• 00:35:00 This video discusses Ted Xiao's work on curriculums for end-to-end robot learning. Xiao describes how a curriculum should be designed to allow for efficient learning of tasks, with a focus on using data sets that are conducive to learning. Another recent work discussed is that of clipboard, which uses a frozen clip bedding as a controller for a robot.
• 00:40:00 Ted Xiao from Google discusses the company's approach to robotics and end-to-end learning, highlighting the benefits of pre-training on internet-based data. He also notes that while data sets for robotics will become increasingly scarce, the use of simulation will allow for the development of more efficient methods that can be applied to real-world scenarios.
• 00:45:00 Ted Xiao from Google speaks about the challenges of training large-scale robots with data that is actionless. He suggests using semantic understanding of the physical world to pre-train policies and then fine-tuning them on larger data sets.
• 00:50:00 Ted Xiao from Google discusses how the company is researching and developing robotic learning methods that are generalizable. Xiao believes that large-scale human data sets are a good place to start, as they provide a more diverse and robust dataset than robot data sets.
• 00:55:00 The video features Ted Xiao, a robotics researcher at Google, discussing the company's efforts to develop "end to end" robot learning. Xiao believes that his work may eventually help large consumer-scale applications of robots.