視覚ベースロボット把持のための確率的マルチモーダルアクターモデル学習
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
画像から成功する把持姿勢の条件付き分布をガウス混合と正規化フローを組み合わせたニューラル密度モデルで直接学習し、CEMより3倍高速な推論を実現した。
著者: Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan, Peter Pastor
分類: cs.RO, cs.LG
原文アブストラクト
Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time to infer the best action from the value network. As a result, the inference time grows exponentially as the dimension of action space increases. We propose an alternative method, by directly training a neural density model to approximate the conditional distribution of successful grasp poses from the input images. We construct a neural network that combines Gaussian mixture and normalizing flows, which is able to represent multi-modal, complex probability distributions. We demonstrate on both simulation and real robot that the proposed actor model achieves similar performance compared to the value network using the Cross-Entropy Method (CEM) for inference, on top-down grasping with a 4 dimensional action space. Our actor model reduces the inference time by 3 times compared to the state-of-the-art CEM method. We believe that actor models will play an important role when scaling up these approaches to higher dimensional action spaces.
