Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data
- Suraj Prakash Pattar, Tsubasa Hirakawa, Takayoshi Yamashita, Tetsuya Sawanobori, Hironobu Fujiyoshi
- IEICE TRANSACTIONS on Information and Systems, Vol. E105-D, No. 9, pp. 1600-1609, 2022
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Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custom-built synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a model-fitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.