Large-Scale Multi-Task Data Collection System This drastically increases the number of tasks the robot can perform (via visual goal specification) and enables more efficient learning of downstream tasks. Actionable Models enables learning in the absence of specific tasks and rewards by training an implicit model of the world that is also an actionable robotic policy. 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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