We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
The work by @cite_16 is designed to compare different interaction strategies for attaining valid grasps using the PR2 robot. In this study, @math participants were asked to grasp as many objects as possible (between @math and @math ) in three rounds of trials. Participants were shown a tutorial before the start of the trial.
@cite_16
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Reliable precision grasping for unknown objects is a prerequisite for robots that work in the field of logistics, manufacturing and household tasks. The nature of this task requires a simultaneous solution of a mixture of sub-problems. These include estimating object properties, finding viable grasps and executing grasps without displacement. We propose to explicitly take perceptual uncertainty into account during grasp execution. The underlying object representation is a probabilistic signed distance field, which includes both signed distances to the surface and spatially interpretable variances. Based on this representation, we propose a two-stage grasp generation method, which is specifically designed for generating precision grasps. In order to evaluate the whole approach, we perform extensive real world grasping experiments on a set of hard-to-grasp objects. Our approach achieves 78 success rate and shows robustness to the placement orientation. We present a system for grasping unknown objects, even from piles or cluttered scenes, given a point cloud. Our method is based on the topography of a given scene and abstracts grasp-relevant structures to enable machine learning techniques for grasping tasks. We describe how Height Accumulated Features HAF and their extension, Symmetry Height Accumulated Features, extract grasp relevant local shapes. We investigate grasp quality using an F-score metric. We demonstrate the gain and the expressive power of HAF by comparing its trained classifier with one that resulted from training on simple height grids. An efficient way to calculate HAF is presented. We describe how the trained grasp classifier is used to explore the whole grasp space and introduce a heuristic to find the most robust grasp. We show how to use our approach to adapt the gripper opening width before grasping. In robotic experiments we demonstrate different aspects of our system on three robot platforms: a Schunk seven-degree-of-freedom arm, a PR2 and a Kuka LWR arm. We perform tasks to grasp single objects, autonomously unload a box and clear the table. Thereby we show that our approach is easily adaptable and robust with respect to different manipulators. As part of the experiments we compare our algorithm with a state-of-the-art method and show significant improvements. Concrete examples are used to illustrate the benefit of our approach compared with established grasp approaches. Finally, we show advantages of the symbiosis between our approach and object recognition. We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations. We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS). The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time. An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements from fingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research. This paper presents a novel robot grasping planning approach that extracts grasp strategies (grasp type, and thumb placement and direction) from human demonstration and integrates them into the grasp planning procedure to generate a feasible grasp concerning the target object geometry and manipulation task. Our study results show that the grasp strategies of grasp type and thumb placement not only represent important human grasp intentions, but also provide meaningful constraints on hand posture and wrist position, which highly reduce both the feasible workspace of a robotic hand and the search space of the grasp planning. This approach has been thoroughly evaluated both in simulation and with a real robotic system for multiple daily living representative objects. We have also demonstrated the robustness of our approach in the experiment with different levels of perception uncertainties. Abstract The problem of finding stable grasps has been widely studied in robotics. However, in many applications the resulting grasps should not only be stable but also applicable for a particular task. Task-specific grasps are closely linked to object categories so that objects in a same category can be often used to perform the same task. This paper presents a probabilistic approach for task-specific stable grasping of objects with shape variations inside the category. An optimal grasp is found as a grasp that is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context. The method requires only partial models of new objects for grasp generation and only few models and example grasps are used during the training stage. The experiments show that the approach can use multiple models to generalize to new objects in that it outperforms grasping based on the closest model. The method is shown to generate stable grasps for new objects belonging to the same class as well as for similar in shape objects of different categories. Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. “Cloud Robotics” describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of “Online Robots” and “Networked Robots”. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80 for the objects in our test set), pose estimation (failure rate under 14 ), and grasping (failure rate under 23 ) and initial results on recall and false positives in larger data sets using confidence measures. Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9 over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6 . We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge. How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping we simply mean grasping by any hand with more than two fingers, such that the robot has some choice about where to place each finger. Such grasps typically fail in one of two ways, either unmodeled objects in the scene cause collisions or object reconstruction is insufficient to ensure that the grasp points provide a stable force closure. These problems can be solved more easily if active sensing is guided by the anticipated actions. Our approach has three stages. First, we take a single view and generate candidate grasps from the resulting partial object reconstruction. Second, we drive the active vision approach to maximise surface reconstruction quality around the planned contact points. During this phase, the anticipated grasp is continually refined. Third, we direct gaze to improve the safety of the planned reach to grasp trajectory. We show, on a dexterous manipulator with a camera on the wrist, that our approach (80.4 success rate) outperforms a randomised algorithm (64.3 success rate). In grasp detection, the robot estimates the position and orientation of potential grasp configurations directly from sensor data. This paper explores the relationship between viewpoint and grasp detection performance. Specifically, we consider the scenario where the approximate position and orientation of a desired grasp is known in advance and we want to select a viewpoint that will enable a grasp detection algorithm to localize it more precisely and with higher confidence. Our main findings are that the right viewpoint can dramatically increase the number of detected grasps and the classification accuracy of the top-n detections. We use this insight to create a viewpoint selection algorithm and compare it against a random viewpoint selection strategy and a strategy that views the desired grasp head-on. We find that the head-on strategy and our proposed viewpoint selection strategy can improve grasp success rates on a real robot by 8 and 4 , respectively. Moreover, we find that the combination of the two methods can improve grasp success rates by as much as 12 . To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table. We use the resulting dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network (GQ-CNN) model that rapidly predicts the probability of success of grasps from depth images, where grasps are specified as the planar position, angle, and depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8sec with a success rate of 93 on eight known objects with adversarial geometry and is 3x faster than registering point clouds to a precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp planner also has the highest success rate on a dataset of 10 novel rigid objects and achieves 99 precision (one false positive out of 69 grasps classified as robust) on a dataset of 40 novel household objects, some of which are articulated or deformable. Code, datasets, videos, and supplementary material are available at this http URL . We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode semantic constraints, which are task-related constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robotic hand can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics along this approach direction. We show experiments planning semantic grasps on everyday objects and executing these grasps with a physical robot. Safe and robust grasping of unknown objects is a major challenge in robotics, which has no general solution yet. A promising approach relies on haptic exploration, where active optimization strategies can be employed to reduce the number of exploration trials. One critical problem is that certain optimal grasps discoverd by the optimization procedure may be very sensitive to small deviations of the parameters from their nominal values: we call these unsafe grasps because small errors during motor execution may turn optimal grasps into bad grasps. To reduce the risk of grasp failure, safe grasps should be favoured. Therefore, we propose a new algorithm, unscented Bayesian optimization, that performs efficient optimization while considering uncertainty in the input space, leading to the discovery of safe optima. The results highlight how our method outperforms the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations, finding robust and safe grasps after a few exploration trials. We propose a planning method for grasping in cluttered environments, where the robot can make simultaneous contact with multiple objects, manipulating them in a deliberate and controlled fashion. This enables the robot to reach for and grasp the target while simultaneously contacting and moving aside obstacles in order to clear a desired path. We use a physicsbased analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. In order to make the problem computationally tractable, we enable multiple simultaneous robot-object interactions, which we pre-compute and cache, but avoid object-object interactions. Tests on large sets of simulated scenes show that our planner produces more successful grasps in more complex scenes than versions that avoid any interaction with surrounding clutter. Validation on a real robot shows that our grasp evaluation method accurately predicts the outcome of a grasp, and that our approach, in conjunction with state-of-the-art object recognition tools, is applicable in reallife scenes that are highly cluttered and constrained. To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table. We use the resulting dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network (GQ-CNN) model that rapidly predicts the probability of success of grasps from depth images, where grasps are specified as the planar position, angle, and depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8sec with a success rate of 93 on eight known objects with adversarial geometry and is 3x faster than registering point clouds to a precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp planner also has the highest success rate on a dataset of 10 novel rigid objects and achieves 99 precision (one false positive out of 69 grasps classified as robust) on a dataset of 40 novel household objects, some of which are articulated or deformable. Code, datasets, videos, and supplementary material are available at http: berkeleyautomation.github.io dex-net . The correct grasp of objects is a key aspect for the right fulfillment of a given task. In robotics, the development of grippers more and more complex and versatile, such as mechanical hands, augments the necessity for algorithms to automatically determine grasps, and in a parallel way, arises the need to quantify the quality of the generated grasps in order to optimize them. This work reviews the quality measures proposed in the grasp literature to quantify the grasp quality. We classify the quality measures into two groups according to the main aspect evaluated by the measure: the location of the contact points on the object or the hand configuration. We also review the approaches that combine different quality measures from the two previous groups to obtain a global quality measure. We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot. We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification. To do so, we make use of a recently developed fine-grained taxonomy of human-object grasps. We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions. Our dataset is different from past work (typically addressed from a robotics perspective) in terms of its scale, diversity, and combination of RGB and depth data. From a computer-vision perspective, our dataset allows for exploration of contact and force prediction (crucial concepts in functional grasp analysis) from perceptual cues. We present extensive experimental results with state-of-the-art baselines, illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis. We demonstrate a near 2X improvement over prior work and a naive deep baseline, while pointing out important directions for improvement.