A Generalist Agent

Abstract

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

A Motion Capture and Imitation Learning Based Approach to Robot Control

“…To more objectively evaluate the performance of the policies obtained with various hyperparameters and on different data sets, a series of quantitative metrics are proposed in this paper. In general, it can be said that, according to these, recurrent networks operating on complete state histories outperform simple deep neural networks operating in a Markovian regime-which is perhaps unsurprising, given the recent successes achieved in applying general-purpose sequence-to-sequence learning methods to the imitation learning domain [19]. As our model precision comparisons rely on indirect estimates rather than empirical data, it is hard to make a direct comparison with state-of-the-art performers in throwing tasks, such as [26], but it should be noted that they use learning at a higher level-outputting parameters that describe each throw-and do not use model outputs to generate Cartesian motion plans directly as proposed here.…”

“…Recurrent neural networks show up in earlier scientific literature periodically, such as in predicting a time-series of robot end-effector loads in an assembly task [17] and learning latent action plans from large, uncategorized play data sets [18]. However, current state-of-the-art performance across a wide variety of sequence prediction tasks-among them being imitation learning in a robotics context-is given by combining a large, universal transformer model with embedding schemes specific to various data modalities [19]. These results strongly suggest that structuring one's approach to be compatible with generalpurpose sequence predictor algorithms is preferable for ensuring its longevity.…”

A Motion Capture and Imitation Learning-based Approach to Robot Control

“…To more objectively evaluate the performance of the policies obtained with various hyperparameters and on different data sets, a series of quantitative metrics are proposed in this paper. In general, it can be said that, according to these, recurrent networks operating on complete state histories outperform simple deep neural networks operating in a Markovian regime -which is perhaps unsurprising, given the recent successes achieved in applying general-purpose sequence-to-sequence learning methods to the imitation learning domain [Reed et al, 2022]. As our model precision comparisons rely on indirect estimates rather than empirical data, it is hard to make a direct comparison with state-of-the-art performers in throwing tasks such as [Zeng et al, 2020] -but it should be noted that they use learning at a higher level -outputting parameters that describe each throw -and do not use model outputs to generate Cartesian motion plans directly as proposed here.…”

“…Recurrent neural networks show up in earlier scientific literature periodically, such as in predicting a time-series of robot end-effector loads in an assembly task [Scherzinger et al, 2019] and learning latent action plans from large, uncategorized play data sets [Lynch et al, 2020]. But current state-of-the-art performance across a wide variety of sequence prediction tasks -among them imitation learning in a robotics context -is given by combining a large, universal transformer model with embedding schemes specific to various data modalities [Reed et al, 2022]. These results strongly suggest that structuring one's approach to be compatible with general-purpose sequence predictor algorithms is preferable for ensuring its longevity.…”

Naive Few-Shot Learning: Sequence Consistency Evaluation

“…Few-shot learners The primary approach today for achieving successful few-shot learning models is to pretrain them over huge relevant and diverse datasets and then fine-tune them for the new tasks (Brown et al, 2020;Reed et al, 2022). The problem with this approach is that the pretrained models become specific to the datasets they were trained on (Li et al, 2017;Nalisnick et al, 2019;Yin et al, 2020;Rajendran et al, 2020).…”