A new paper from Google Brain and X using PyBullet:
\nearning-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.<\/p>\n
See also https:\/\/sites.google.com\/corp\/view\/multi-task-domain-adaptation<\/a> and A new paper from Google Brain and X using PyBullet: earning-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 … Continue reading Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation<\/span>
\nhttps:\/\/arxiv.org\/abs\/1710.06422<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"