Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing finger. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline, which incrementally introduce reasoning about object poses and corrective manipulation actions.
 This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al- (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches. We present a fully autonomous robotic system for grasping objects in dense clutter. The objects are unknown and have arbitrary shapes. Therefore, we cannot rely on prior models. Instead, the robot learns online, from scratch, to manipulate the objects by trial and error. Grasping objects in clutter is significantly harder than grasping isolated objects, because the robot needs to push and move objects around in order to create sufficient space for the fingers. These pre-grasping actions do not have an immediate utility, and may result in unnecessary delays. The utility of a pre-grasping action can be measured only by looking at the complete chain of consecutive actions and effects. This is a sequential decision-making problem that can be cast in the reinforcement learning framework. We solve this problem by learning the stochastic transitions between the observed states, using nonparametric density estimation. The learned transition function is used only for re-calculating the values of the executed actions in the observed states, with different policies. Values of new state-actions are obtained by regressing the values of the executed actions. The state of the system at a given time is a depth (3D) image of the scene. We use spectral clustering for detecting the different objects in the image. The performance of our system is assessed on a robot with real-world objects.  This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity, equilibrium, stability, and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands.Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one or more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the internal grasping forces. Stability algorithms aim at achiev ing positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces ... This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The light- weight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83 grasp success rate on a set of previously unseen objects with adversarial geometry and 88 on a set of household objects that are moved during the grasp attempt. We also achieve 81 accuracy when grasping in dynamic clutter. Manipulating natural objects of irregular shapes, such as rocks, is an essential capability of robots operating in outdoor environments. Physics-based simulators are commonly used to plan stable grasps for man-made objects. However, planning is an expensive process that is based on simulating hand and object trajectories in different configurations, and evaluating the outcome of each trajectory. This problem is particularly concerning when the objects are irregular or cluttered, because the space of feasible grasps is significantly smaller, and more configurations need to be evaluated before finding a good one. In this paper, we first present a learning technique for fast detection of an initial set of potentially stable grasps in a cluttered scene. The best detected grasps are further optimized by fine-tuning the configuration of the hand in simulation. To reduce the computational burden of this last operation, we model the outcomes of the grasps as a Gaussian Process, and use an entropy-search method in order to focus the optimization on regions where the best grasp is most likely to be. This approach is tested on the task of clearing piles of real, unknown, rock debris with an autonomous robot. Empirical results show a clear advantage of the proposed approach when the time window for decision is short.