触覚から外部接触を知覚することで挿入ポリシーの学習が改善する
Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies
グリッパと物体間の触覚から物体と環境の外部接触を推定するNCFを改良し、実機で機能するようにして挿入タスクのポリシー学習に用いたところ、成功率と速度が向上した。
著者: Carolina Higuera, Joseph Ortiz, Haozhi Qi, Luis Pineda, Byron Boots, Mustafa Mukadam
分類: cs.RO
原文アブストラクト
Robotic manipulation tasks such as object insertion typically involve interactions between object and environment, namely extrinsic contacts. Prior work on Neural Contact Fields (NCF) use intrinsic tactile sensing between gripper and object to estimate extrinsic contacts in simulation. However, its effectiveness and utility in real-world tasks remains unknown. In this work, we improve NCF to enable sim-to-real transfer and use it to train policies for mug-in-cupholder and bowl-in-dishrack insertion tasks. We find our model NCF-v2, is capable of estimating extrinsic contacts in the real-world. Furthermore, our insertion policy with NCF-v2 outperforms policies without it, achieving 33% higher success and 1.36x faster execution on mug-in-cupholder, and 13% higher success and 1.27x faster execution on bowl-in-dishrack.
