1k citations · 4.2k across the 34 of their papers we have counts for
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Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
David Venuto, Sherry Yang, Pieter Abbeel +3
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learnin…
RT-1: Robotics Transformer for Real-World Control at Scale
Anthony Brohan, Noah Brown, Justice Carbajal +48
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specif…
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models
Hao Liu, Xinyang Geng, Lisa Lee +4
Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing imp…
Inner Monologue: Embodied Reasoning through Planning with Language Models
Wenlong Huang, Fei Xia, Ted Xiao +14
Recent works have shown how the reasoning capabilities of Large Language Models (LLMs) can be applied to domains beyond natural language processing, such as planning and interactio…
Multi-Game Decision Transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang +8
A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largel…
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak +1
Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks,…