どこを通っても関係ない：マルチタイムスケール行動解析のための新しい自己教師あり手法
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
行動予測の分布を予測する目的関数と、短期・長期ダイナミクスを別々の潜在空間で扱うマルチスケール構造を組み合わせた自己教師あり表現学習を提案し、MABe 2022チャレンジで総合1位を獲得した。
著者: Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer
分類: cs.LG, cs.RO
原文アブストラクト
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future. While some success has been found in building representations of behavior under constrained or simplified task-based conditions, many of these models cannot be applied to free and naturalistic settings where behavior becomes increasingly hard to model. In this work, we develop a multi-task representation learning model for behavior that combines two novel components: (i) An action prediction objective that aims to predict the distribution of actions over future timesteps, and (ii) A multi-scale architecture that builds separate latent spaces to accommodate short- and long-term dynamics. After demonstrating the ability of the method to build representations of both local and global dynamics in realistic robots in varying environments and terrains, we apply our method to the MABe 2022 Multi-agent behavior challenge, where our model ranks 1st overall and on all global tasks, and 1st or 2nd on 7 out of 9 frame-level tasks. In all of these cases, we show that our model can build representations that capture the many different factors that drive behavior and solve a wide range of downstream tasks.
