Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationInstance SegmentationSemantic SegmentationTransfer LearningSimilar Papers 제목 키워드 기반
Attribute-Based Robotic Grasping with Data-Efficient Adaptation
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. …
AttributeRobotic GraspingAttribute-Based Robotic Grasping with One-Grasp Adaptation
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper at…
AttributeObjectRobotic GraspingGrasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation
Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directl…
Domain AdaptationSemi-supervised Domain AdaptationUsing Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to re…
Domain AdaptationIndustrial RobotsRobotic GraspingData-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks
Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in the real world, the policy needs to see …
3D Shape RepresentationObjectRobotic GraspingRobot Manipulation