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Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition

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arxiv 2303.13512 v1 pith:T7QLPXA2 submitted 2023-03-23 cs.AI

Stephanie Milani , Anssi Kanervisto , Karolis Ramanauskas , Sander Schulhoff , Brandon Houghton , Sharada Mohanty , Byron Galbraith , Ke Chen
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Yan Song Tianze Zhou Bingquan Yu He Liu Kai Guan Yujing Hu Tangjie Lv Federico Malato Florian Leopold Amogh Raut Ville Hautamäki Andrew Melnik Shu Ishida João F. Henriques Robert Klassert Walter Laurito Ellen Novoseller Vinicius G. Goecks Nicholas Waytowich David Watkins Josh Miller Rohin Shah
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classification cs.AI
keywords competitionfeedbackhumanbasaltalgorithmsfine-tuningminerltasks
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To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022. The BASALT challenge asks teams to compete to develop algorithms to solve tasks with hard-to-specify reward functions in Minecraft. Through this competition, we aimed to promote the development of algorithms that use human feedback as channels to learn the desired behavior. We describe the competition and provide an overview of the top solutions. We conclude by discussing the impact of the competition and future directions for improvement.

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  1. STEVE-Audio: Expanding the Goal Conditioning Modalities of Embodied Agents in Minecraft

    cs.LG 2024-12 conditional novelty 4.0 of 10

    An audio-conditioned STEVE-1 agent, built with a new Minecraft audio-video CLIP model and a learned prior, matches or beats text- and video-conditioned versions on most short-horizon collection tasks.

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