Fast graspability evaluation on single depth maps for bin picking with general grippers

Domae, Yukiyasu; Okuda, Hiroyuki; Taguchi, Yuichi; Kim, Su-Mi; Hirai, Takashi

doi:10.1109/icra.2014.6907124

Cited by 107 publications

(76 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The waste sorting problem involves a manipulation task similar to the more studied problems of "cleaning a table by grasping" [2] and bin picking [3], [4], [5], but also differs from them in several aspects:…”

Section: B the Robotic Waste Sorting Problemmentioning

confidence: 99%

Classifying and sorting cluttered piles of unknown objects with robots: A learning approach

Kujala¹,

Lukka²,

Holopainen³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

We consider the problem of sorting a densely cluttered pile of unknown objects using a robot. This yet unsolved problem is relevant in the robotic waste sorting business.By extending previous active learning approaches to grasping, we show a system that learns the task autonomously. Instead of predicting just whether a grasp succeeds, we predict the classes of the objects that end up being picked and thrown onto the target conveyor. Segmenting and identifying objects from the uncluttered target conveyor, as opposed to the working area, is easier due to the added structure since the thrown objects will be the only ones present.Instead of trying to segment or otherwise understand the cluttered working area in any way, we simply allow the controller to learn a mapping from an RGBD image in the neighborhood of the grasp to a predicted result-all segmentation etc. in the working area is implicit in the learned function. The grasp selection operates in two stages: The first stage is hardcoded and outputs a distribution of possible grasps that sometimes succeed. The second stage uses a purely learned criterion to choose the grasp to make from the proposal distribution created by the first stage.In an experiment, the system quickly learned to make good pickups and predict correctly, in advance, which class of object it was going to pick up and was able to sort the objects from a densely cluttered pile by color.

show abstract

Section: B the Robotic Waste Sorting Problemmentioning

confidence: 99%

Classifying and sorting cluttered piles of unknown objects with robots: A learning approach

Kujala¹,

Lukka²,

Holopainen³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…While grasp planning searches for gripper configurations that maximize a quality metric derived from mechanical wrench space analysis [24], human labels [28], or selfsupervised labels [20], suction grasps are often planned directly on point clouds using heuristics such as grasping near the object centroid [10] or at the center of planar surfaces [4], [5]. These heuristics work well for prismatic objects such as boxes and cylinders but may fail on objects with non-planar surfaces near the object centroid, which is common for industrial parts and household objects such as staplers or children's toys.…”

Section: Introductionmentioning

confidence: 99%

Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Mahler¹,

Matl²,

Liu³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

235

191

View full text Add to dashboard Cite

Vacuum-based end effectors are widely used in industry and are often preferred over parallel-jaw and multifinger grippers due to their ability to lift objects with a single point of contact. Suction grasp planners often target planar surfaces on point clouds near the estimated centroid of an object. In this paper, we propose a compliant suction contact model that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of the suction grasp to resist an external gravity wrench. To characterize grasps, we estimate robustness to perturbations in end-effector and object pose, material properties, and external wrenches. We analyze grasps across 1,500 3D object models to generate Dex-Net 3.0, a dataset of 2.8 million point clouds, suction grasps, and grasp robustness labels. We use Dex-Net 3.0 to train a Grasp Quality Convolutional Neural Network (GQ-CNN) to classify robust suction targets in point clouds containing a single object. We evaluate the resulting system in 350 physical trials on an ABB YuMi fitted with a pneumatic suction gripper. When evaluated on novel objects that we categorize as Basic (prismatic or cylindrical), Typical (more complex geometry), and Adversarial (with few available suction-grasp points) Dex-Net 3.0 achieves success rates of 98%, 82%, and 58% respectively, improving to 81% in the latter case when the training set includes only adversarial objects. Code, datasets, and supplemental material can be found at http://berkeleyautomation.github.io/dex-net.

show abstract

“…As for the research on bin-picking method, Ghita and Whalan [5] proposed to pick the top most object of the pile. Domae et al [9] proposed a method for determining the grasping pose of an object directly from the depth image of the pile. Some researchers such as [7], [10]- [12] proposed methods for identifying the poses of multiple objects of the pile and picking one of them by using a grasp planning method.…”

Section: Introductionmentioning

confidence: 99%

Learning Based Industrial Bin-Picking Trained with Approximate Physics Simulator

Matsumura

Harada

Domae

et al. 2018

Intelligent Autonomous Systems 15

Self Cite

View full text Add to dashboard Cite

In this research, we tackle the problem of picking an object from randomly stacked pile. Since complex physical phenomena of contact among objects and fingers makes it difficult to perform the bin-picking with high success rate, we consider introducing a learning based approach. For the purpose of collecting enough number of training data within a reasonable period of time, we introduce a physics simulator where approximation is used for collision checking. In this paper, we first formulate the learning based robotic bin-picking by using CNN (Convolutional Neural Network). We also obtain the optimum grasping posture of parallel jaw gripper by using CNN. Finally, we show that the effect of approximation introduced in collision checking is relaxed if we use exact 3D model to generate the depth image of the pile as an input to CNN.

show abstract

Fast graspability evaluation on single depth maps for bin picking with general grippers

Cited by 107 publications

References 19 publications

Classifying and sorting cluttered piles of unknown objects with robots: A learning approach

Classifying and sorting cluttered piles of unknown objects with robots: A learning approach

Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning

Learning Based Industrial Bin-Picking Trained with Approximate Physics Simulator

Contact Info

Product

Resources

About