Additionally, it enables robots to master new tasks quickly through use of its extensive multi-task dataset (new task fine-tuning in <1 day of data collection). MT-Opt introduces a scalable data-collection mechanism that is used to collect over 800,000 episodes of various tasks on real robots and demonstrates a successful application of multi-task RL that yields ~3x average improvement over baseline. Today we present two new advances for robotic RL at scale, MT-Opt, a new multi-task RL system for automated data collection and multi-task RL training, and Actionable Models, which leverages the acquired data for goal-conditioned RL. Automating this process is a large engineering endeavour, and effectively reusing past robotic data collected by different robots remains an open problem. However, because robots collect their own data, robotic skill learning presents a unique set of opportunities and challenges. For example, pre-training on large natural language datasets can enable few- or zero-shot learning of multiple tasks, such as question answering and sentiment analysis. In other large-scale machine learning domains, such as natural language processing and computer vision, a number of strategies have been applied to amortize the effort of learning over multiple skills. Multi-task data collection across multiple robots where different robots collect data for different tasks. Thus, the computational costs of building general-purpose everyday robots using current robot learning methods becomes prohibitive as the number of tasks grows. But training even a single task (e.g., grasping) using offline reinforcement learning (RL), a trial and error learning method where the agent uses training previously collected data, can take thousands of robot-hours, in addition to the significant engineering needed to enable autonomous operation of a large-scale robotic system. Posted by Karol Hausman, Senior Research Scientist and Yevgen Chebotar, Research Scientist, Robotics at Googleįor general-purpose robots to be most useful, they would need to be able to perform a range of tasks, such as cleaning, maintenance and delivery.
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