Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd

Robust Scene Estimation for Goal-directed Robotic Manipulation in Unstructured Environments

To make autonomous robots "taskable" so that they function properly and interact fluently with human partners, they must be able to perceive and understand the semantic aspects of their environments. More specifically, they must know what objects exist and where they are in the unstructured human world. Progresses in robot perception, especially in deep learning, have greatly improved for detecting and localizing objects. However, it still remains a challenge for robots to perform a highly reliable scene estimation in unstructured environments that is determined by robustness, adaptability and scale. In this dissertation, we address the scene estimation problem under uncertainty, especially in unstructured environments. We enable robots to build a reliable object-oriented representation that describes objects present in the environment, as well as inter-object spatial relations. Specifically, we focus on addressing following challenges for reliable scene estimation: 1) robust perception under uncertainty results from noisy sensors, objects in clutter and perceptual aliasing, 2) adaptable perception in adverse conditions by combined deep learning and probabilistic generative methods, 3) scalable perception as the number of objects grows and the structure of objects becomes more complex (e.g. objects in dense clutter). Towards realizing robust perception, our objective is to ground raw sensor observations into scene states while dealing with uncertainty from sensor measurements and actuator control . Scene states are represented as scene graphs, where scene graphs denote parameterized axiomatic statements that assert relationships between objects and their poses. To deal with the uncertainty, we present a pure generative approach, Axiomatic Scene Estimation (AxScEs). AxScEs estimates a probabilistic distribution across plausible scene graph hypotheses describing the configuration of objects. By maintaining a diverse set of possible states, the proposed approach demonstrates the robustness to the local minimum in the scene graph state space and effectiveness for manipulation-quality perception based on edit distance on scene graphs. To scale up to more unstructured scenarios and be adaptable to adversarial scenarios, we present Sequential Scene Understanding and Manipulation (SUM), which estimates the scene as a collection of objects in cluttered environments. SUM is a two-stage method that leverages the accuracy and efficiency from convolutional neural networks (CNNs) with probabilistic inference methods. Despite the strength from CNNs, they are opaque in understanding how the decisions are made and fragile for generalizing beyond overfit training samples in adverse conditions (e.g., changes in illumination). The probabilistic generative method complements these weaknesses and provides an avenue for adaptable perception. To scale up to densely cluttered environments where objects are physically touching with severe occlusions, we present GeoFusion, which fuses noisy observations from multiple frames by exploring geometric consistency at object level. Geometric consistency characterizes geometric compatibility between objects and geometric similarity between observations and objects. It reasons about geometry at the object-level, offering a fast and reliable way to be robust to semantic perceptual aliasing. The proposed approach demonstrates greater robustness and accuracy than the state-of-the-art pose estimation approach.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163060/1/zsui_1.pd

Multimodal Grasp Planner for Hybrid Grippers in Cluttered Scenes

Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid grippers that allows for multiple grasping modalities. In particular, the planner manages two-finger grasps, single or double suction grasps, and magnetic grasps. Grasps for different modalities are geometrically computed based on the cuboid and the material properties of the objects in the clutter. The presented framework is modular and can leverage any 6D pose estimation or material segmentation network as far as they satisfy the required interface. Furthermore, the planner can be applied to any (hybrid) gripper, provided the gripper clearance, finger width, and suction diameter. The approach is fast and has a low computational burden, as it uses geometric computations for grasp synthesis and selection. The performance of the system has been assessed with an experimental campaign in three manipulation scenarios of increasing difficulty using the objects of the YCB dataset and the DLR hybrid-compliant gripper

MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.Comment: 10 pages, 10 figures, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 202

Deep learning for object detection in robotic grasping contexts

Dans la dernière décennie, les approches basées sur les réseaux de neurones convolutionnels sont devenus les standards pour la plupart des tâches en vision numérique. Alors qu'une grande partie des méthodes classiques de vision étaient basées sur des règles et algorithmes, les réseaux de neurones sont optimisés directement à partir de données d'entraînement qui sont étiquetées pour la tâche voulue. En pratique, il peut être difficile d'obtenir une quantité su sante de données d'entraînement ou d'interpréter les prédictions faites par les réseaux. Également, le processus d'entraînement doit être recommencé pour chaque nouvelle tâche ou ensemble d'objets. Au final, bien que très performantes, les solutions basées sur des réseaux de neurones peuvent être difficiles à mettre en place. Dans cette thèse, nous proposons des stratégies visant à contourner ou solutionner en partie ces limitations en contexte de détection d'instances d'objets. Premièrement, nous proposons d'utiliser une approche en cascade consistant à utiliser un réseau de neurone comme pré-filtrage d'une méthode standard de "template matching". Cette façon de faire nous permet d'améliorer les performances de la méthode de "template matching" tout en gardant son interprétabilité. Deuxièmement, nous proposons une autre approche en cascade. Dans ce cas, nous proposons d'utiliser un réseau faiblement supervisé pour générer des images de probabilité afin d'inférer la position de chaque objet. Cela permet de simplifier le processus d'entraînement et diminuer le nombre d'images d'entraînement nécessaires pour obtenir de bonnes performances. Finalement, nous proposons une architecture de réseau de neurones ainsi qu'une procédure d'entraînement permettant de généraliser un détecteur d'objets à des objets qui ne sont pas vus par le réseau lors de l'entraînement. Notre approche supprime donc la nécessité de réentraîner le réseau de neurones pour chaque nouvel objet.In the last decade, deep convolutional neural networks became a standard for computer vision applications. As opposed to classical methods which are based on rules and hand-designed features, neural networks are optimized and learned directly from a set of labeled training data specific for a given task. In practice, both obtaining sufficient labeled training data and interpreting network outputs can be problematic. Additionnally, a neural network has to be retrained for new tasks or new sets of objects. Overall, while they perform really well, deployment of deep neural network approaches can be challenging. In this thesis, we propose strategies aiming at solving or getting around these limitations for object detection. First, we propose a cascade approach in which a neural network is used as a prefilter to a template matching approach, allowing an increased performance while keeping the interpretability of the matching method. Secondly, we propose another cascade approach in which a weakly-supervised network generates object-specific heatmaps that can be used to infer their position in an image. This approach simplifies the training process and decreases the number of required training images to get state-of-the-art performances. Finally, we propose a neural network architecture and a training procedure allowing detection of objects that were not seen during training, thus removing the need to retrain networks for new objects