Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.
The work in REF presents a multi-task domain adaptation framework to transfer the learned targetoriented grasping policy from simulation to the real world.
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
2018 IEEE International Conference on Robotics and Automation (ICRA)
2018 IEEE International Conference on Robotics and Automation (ICRA)
Mathematics Computer Science Engineering