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

   Mahler, J. ; Matl, M. ; Liu, X. ; Li, A. ; Gealy, D. ; Goldberg, K. ( January 2018 , IEEE International Conference on Robotics and Automation)

   Vacuum-based end effectors are widely used in in- dustry 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 andmore »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 eval- uated 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.« less
2. Using Geometric Features to Represent Near-Contact Behavior in Robotic Grasping

   https://doi.org/10.1109/ICRA.2019.8793779  

   Dessalene, Eadom ; Ong, Yi Herng ; Morrow, John ; Balasubramanian, Ravi ; Grimm, Cindy ( May 2019 , International Conference on Robotics and Applications)

   In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I.e., they capture much ofmore »the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps.« less
3. Tri-modal thin-film flexible electronic skin to augment robotic grasping

   https://doi.org/10.1109/MEMSYS.2018.8346698  

   Yu, Caroline ; Cavallari, Marco R. ; Kymissis, Ioannis ( January 2018 , MEMS 2018)

   Robotic grasping is successful when a robot can sense and grasp an object without letting it slip. Beyond industrial robotic tasks, there are two main robotic grasping methods. The first is planning-based grasping where the object geometry is known beforehand and stable grasps are calculated using algorithms [1]. The second uses tactile feedback. Currently, there are capacitive sensors placed beneath stiff pads on the front of robotic fingers [2]. With post-execution grasp adjustment procedures to estimate grasp stability, a support vector machine classifier can distinguish stable and unstable grasps. The accuracy across the classes of tested objects is 81% [1].more »We are proposing to improve the classifier's accuracy by wrapping flexible sensors around the robotic finger to gain information from the edges and sides of the finger.« less
4. Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences

   Mahler, J. ; Goldberg, K. ( January 2017 , CoRL)

   Recent results suggest that it is possible to grasp a variety of singu- lated objects with high precision using Convolutional Neural Networks (CNNs) trained on synthetic data. This paper considers the task of bin picking, where multiple objects are randomly arranged in a heap and the objective is to sequen- tially grasp and transport each into a packing box. We model bin picking with a discrete-time Partially Observable Markov Decision Process that specifies states of the heap, point cloud observations, and rewards. We collect synthetic demon- strations of bin picking from an algorithmic supervisor uses full state information to optimizemore »for the most robust collision-free grasp in a forward simulator based on pybullet to model dynamic object-object interactions and robust wrench space analysis from the Dexterity Network (Dex-Net) to model quasi-static contact be- tween the gripper and object. We learn a policy by fine-tuning a Grasp Quality CNN on Dex-Net 2.1 to classify the supervisor’s actions from a dataset of 10,000 rollouts of the supervisor in the simulator with noise injection. In 2,192 physical trials of bin picking with an ABB YuMi on a dataset of 50 novel objects, we find that the resulting policies can achieve 94% success rate and 96% average preci- sion (very few false positives) on heaps of 5-10 objects and can clear heaps of 10 objects in under three minutes. Datasets, experiments, and supplemental material are available at http://berkeleyautomation.github.io/dex-net.« less
5. Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter

   https://doi.org/10.1007/s10514-020-09907-y  

   Wu, Bohan ; Akinola, Iretiayo ; Gupta, Abhi ; Xu, Feng ; Varley, Jacob ; Watkins-Valls, David ; Allen, Peter K. ( February 2020 , Autonomous Robots)

   Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger joint angles of the hand. Trained purely in simulation, this framework skillfully closes the sim-to-real gap. To close the visual sim-to-real gap, this framework uses a single depth imagemore »as input. To close the dynamics sim-to-real gap, this framework circumvents continuous motor control with a direct mapping from pixel to Cartesian space inferred from the same depth image. Finally, this framework demonstrates inter-robot generality by achieving over 92% real-world grasp success rates in cluttered scenes with novel objects using two multi-fingered robotic hand-arm systems with different degrees of freedom.« less