空間・相互作用ダイナミクスの構造表現学習による混雑環境での軌道予測
Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
環境の空間的・動的な側面を教師なしで学習し、歩行者予測やロボット操作の軌道予測をラベルなし画像データのみで新環境・新タスクへ適応できるモジュール型手法を提案した。
著者: Todor Davchev, Michael Burke, Subramanian Ramamoorthy
分類: cs.LG, cs.CV, cs.MA, cs.RO, stat.ML
原文アブストラクト
Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a learned model of the environment for motion prediction. This modularity explicitly allows for unsupervised adaptation of trajectory prediction models to unseen environments and new tasks by relying on unlabelled image data only. We model both the spatial and dynamic aspects of a given environment alongside the per agent motions. This results in more informed motion prediction and allows for performance comparable to the state-of-the-art. We highlight the model's prediction capability using a benchmark pedestrian prediction problem and a robot manipulation task and show that we can transfer the predictor across these tasks in a completely unsupervised way. The proposed approach allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments.
