想像せよ！創発的アフォーダンスを活用した3Dツール合成
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis
視覚ベースの生成モデルとタスク性能予測器を組み合わせ、潜在空間を勾配探索することで、リーチング課題に適した棒状ツールの形状を生成する手法を提案した。
著者: Yizhe Wu, Sudhanshu Kasewa, Oliver Groth, Sasha Salter, Li Sun, Oiwi Parker Jones, Ingmar Posner
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
In this paper we explore the richness of information captured by the latent space of a vision-based generative model. The model combines unsupervised generative learning with a task-based performance predictor to learn and to exploit task-relevant object affordances given visual observations from a reaching task, involving a scenario and a stick-like tool. While the learned embedding of the generative model captures factors of variation in 3D tool geometry (e.g. length, width, and shape), the performance predictor identifies sub-manifolds of the embedding that correlate with task success. Within a variety of scenarios, we demonstrate that traversing the latent space via backpropagation from the performance predictor allows us to imagine tools appropriate for the task at hand. Our results indicate that affordances-like the utility for reaching-are encoded along smooth trajectories in latent space. Accessing these emergent affordances by considering only high-level performance criteria (such as task success) enables an agent to manipulate tool geometries in a targeted and deliberate way.
