Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
arXiv:1710.06422
Abstract
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.
arXiv commentICRA 2018
References in corpus (9)
- Instance Normalization: The Missing Ingredient for Fast Stylization
- Learning Transferable Features with Deep Adaptation Networks
- Deep Domain Confusion: Maximizing for Domain Invariance
- Domain Separation Networks
- Hindsight Experience Replay
- Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
- Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
- Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
- Learning a visuomotor controller for real world robotic grasping using simulated depth images