触覚で操る：深層予測モデルによる触覚ベース制御
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
高解像度触覚センサと深層ニューラルネットのダイナミクスモデルを組み合わせ、生の触覚入力から触覚サーボを教師なしで学習する枠組みを提案し、ボールやサイコロなどを目標触覚状態へ操作できることを示した。
著者: Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc
