用深度学习实现多指手部触觉状态约束下的精准开盖操作
Focused Blind Switching Manipulation Based on Constrained and Regional Touch States of Multi-Fingered Hand Using Deep Learning
- 基于触觉状态约束的注意力机制，聚焦关键感知模态
- 真实开盖任务成功率显著提升，适配多种物体
- 模型自动学习各子任务特征并动态关注重要信息
为实现期望的抓取姿态（包括物体位置与朝向），需根据当前触觉状态调整多指运动。当微调物体状态时，不仅本体感觉，全手触觉信息也至关重要。然而，高自由度多指运动与丰富触觉信息的切换仍具挑战。本文提出一种考虑触觉状态约束的损失函数及注意力机制，用于聚焦关键感知模态。采用由自编码器（AE）压缩触觉信息、长短期记忆网络（LSTM）依据触觉状态切换动作的策略模型（AE-LSTM）。以开瓶盖为任务目标，包含滑动与拧盖两个子任务。实验结果表明，该方法在真实场景下对多种物体实现了最优开盖成功率；且模型成功学习到各子任务特征，并能动态关注特定感知模态。
原文摘要 · Abstract (English)
To achieve a desired grasping posture (including object position and orientation), multi-finger motions need to be conducted according to the the current touch state. Specifically, when subtle changes happen during correcting the object state, not only proprioception but also tactile information from the entire hand can be beneficial. However, switching motions with high-DOFs of multiple fingers and abundant tactile information is still challenging. In this study, we propose a loss function with constraints of touch states and an attention mechanism for focusing on important modalities depending on the touch states. The policy model is AE-LSTM which consists of Autoencoder (AE) which compresses abundant tactile information and Long Short-Term Memory (LSTM) which switches the motion depending on the touch states. Motion for cap-opening was chosen as a target task which consists of subtasks of sliding an object and opening its cap. As a result, the proposed method achieved the best success rates with a variety of objects for real time cap-opening manipulation. Furthermore, we could confirm that the proposed model acquired the features of each subtask and attention on specific modalities.
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