Using Geometry to Detect Grasps in 3D Point Clouds
arXiv:1501.03100
Abstract
This paper proposes a new approach to detecting grasp points on novel objects presented in clutter. The input to our algorithm is a point cloud and the geometric parameters of the robot hand. The output is a set of hand configurations that are expected to be good grasps. Our key idea is to use knowledge of the geometry of a good grasp to improve detection. First, we use a geometrically necessary condition to sample a large set of high quality grasp hypotheses. We were surprised to find that using simple geometric conditions for detection can result in a relatively high grasp success rate. Second, we use the notion of an antipodal grasp (a standard characterization of a good two fingered grasp) to help us classify these grasp hypotheses. In particular, we generate a large automatically labeled training set that gives us high classification accuracy. Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter. This system is available as a ROS package at http://wiki.ros.org/agile_grasp.
References in corpus (1)
Cited by in corpus (7)
- REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
- A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
- Generative grasp synthesis from demonstration using parametric mixtures
- Domain Independent Unsupervised Learning to grasp the Novel Objects
- Experiments on Learning Based Industrial Bin-picking with Iterative Visual Recognition
- Optimized edge-based grasping method for a cluttered environment
- Associating Grasp Configurations with Hierarchical Features in Convolutional Neural Networks