Pose error robust grasping from contact wrench space metrics

By Jonathan Weisz, Peter K. Allen. 2012

203 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Robotics & Control

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Citations in the corpus

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018, background): “Real-world robotic manipulation requires broad generalization, and indeed much of the research on robotic grasping has sought to achieve such generalization, either through the use of grasp metrics based on first principles [20] or learning [21, 10], with the latter class of methods achieving some o”
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, background): “Grasping too, has been well studied in the domain of model-based reasoning [19]; from modeling contact forces and their resistance to external wrenches [20], [21], to characterizing grasps by their ability to constrain object mobility [22].”
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “Geometric methods analyze the shape of a target object and plan a suitable grasp pose, based on criteria such as force closure (Weisz and Allen, 2012) or caging (Rodriguez et al.”
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, methodology): “It should be noted that we use both the robot arms to collect the data more quickly.”
Deep learning for detecting robotic grasps (2013, background): “Grasping Given 3D Model: Fast synthesis of grasps for known 3D models remains an active research topic [14, 20, 65], with recent methods using advanced physical simulation to find optimal grasps.”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, background): “labeling grasps on 3D object models [61] in a database with robust metrics such as probability of force closure [27] due to the computational complexity of sampling.”
Supervision via competition: Robot adversaries for learning tasks (2016, background): “However, later work found such metrics unstable under shaking [37] and pose uncertainty [38], [39].”
Data-Driven Grasp Synthesis—A Survey (2013, background): “Only recently, several articles [21, 22] have analyzed this question and come to the conclusion that the classic metrics are not good predictors for grasp success in the real world.”
Learning task-oriented grasping for tool manipulation from simulated self-supervision (2018, background): “thods approach the grasping problem from a purely geometric perspective. They optimize grasp quality measures based on analytic models of geometric constraints, such as force closure and form closure [12, 44, 52]. While grasp quality measures offer a principled mathematical framework of grasp analys”
Learning ambidextrous robot grasping policies (2019, background): “One approach to UP is to create a database of grasps on threedimensional (3D) object models using grasp performance metrics derived from geometry and physics (1, 2) with stochastic sampling to model uncertainty (3, 4).”
Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, methodology): “…Grasp dataset [67], the MIT KIT object dataset [88], and the Willow Garage Household Objects Database [40] are available online and have been used to evaluate different aspects of grasping algorithms, including grasp stability [45], [46], robust grasping [162], and scene understanding [128].”
Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning (2017, background): “A common approach iis to select a configuration that maximizes a quality metric (or reward) based on wrench space analysis [25], robustness to perturbations [34], or a model learned from human labels [29] or self-supervision [26].”
Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations (2019, background): “Classic vision-based grasping solutions often explicitly model contact forces with prior knowledge of object geometry, pose, and dynamics [25, 31, 6, 38].”
A Survey of Research on Cloud Robotics and Automation (2015, background): “New approaches are required that are robust to dirty data.”
Learning a Decentralized Multi-arm Motion Planner (2020, unknown): “[38] with noise [39].”
Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences (2017, methodology): “002 [3] and the gripper does not collide with the table or object along a linear approach trajectory [25, 21].”
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning (2018, background): “A common approach is to select a configuration that maximizes a quality metric (or reward) based on wrench space analysis [24], robustness to perturbations [32], or a model learned from human labels [14] or self-supervision [25].”
Domain Randomization and Generative Models for Robotic Grasping (2017, methodology): “Some methods attempt to make these estimates more robust to gripper and object pose uncertainty and sensor error by instead maximizing the expected value of a metric under uncertainty [15], [50].”
Leveraging big data for grasp planning (2015, background): “2) Computation of Ratio Metrics: A popular way to enhance metrics that are computed in simulation is to simulate uncertainty in object pose [17] [37].”
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods (2018, background): “Prior work in robotic manipulation has sought to address the grasping problem through a wide range of methods, from analytic grasp metrics [43], [36] to learning-based approaches [2].”