視覚と触覚を統合する自己教師ありマルチモーダル表現学習による接触の多い操作
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
視覚と触覚の入力を自己教師あり学習でコンパクトなマルチモーダル表現にまとめ、強化学習のサンプル効率を高めてペグ挿入タスクを実機でも安定してこなせるようにした研究。
著者: Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. Results for simulated and real robot experiments are presented.
関連論文
- 触覚と画像の深層ゲート付きマルチモーダル学習による把持中物体の姿勢変化推定マルチモーダル/触覚
