A New Approach to Visual Servoing in Robotics

By B. Espiau, François Chaumette, Patrick Rives. 1992

Subfields: Reinforcement Learning, Robotics & Control

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Citations in the corpus

End-to-End Training of Deep Visuomotor Policies (2015, methodology): “The goal of our approach is also similar to visual servoing, which performs feedback control on feature points in a camera image [6, 27, 42].”
Deep visual foresight for planning robot motion (2016, methodology): “Our approach is related to visual servoing, which performs feedback control on features in an image [32], [33], [34] or the image pixels themselves [35].”
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “…methods are typically concerned with reaching a pose relative to objects in the scene, and often (though not always) rely on manually designed or specified features for feedback control (Espiau et al., 1992; Wilson et al., 1996; Vahrenkamp et al., 2008; Hebert et al., 2012; Mohta et al., 2014).”
Deep spatial autoencoders for visuomotor learning (2015, methodology): “The goal of our method is similar to that of visual servoing, which performs feedback control on image features [24],”
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning (2018, background): “Traditional approaches aim to minimize distances of feature points [17, 18, 19], or pixel intensities [20].”
Visual servoing of an under-actuated dynamic rigid-body system: an image-based approach (2002, methodology): “In such situations, the dynamics of the system are easily compensated using a computed torque (or high gain) control design and the visual servo control may be derived from a first-order model of the image dynamics [7].”
Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders (2015, background): “ing, which performs feedback control on image features [24], [25], [26].”
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation (2019, background): “…that can execute complex behaviours from only pixel inputs has been a well studied problem, for example with visual servoing (Mohta et al., 2014; Espiau et al., 1992; Wilson et al., 1996; Yoshimi & Allen, 1994; Jagersand et al., 1997; Lampe & Riedmiller, 2013; Sadeghi et al., 2018; Sadeghi,…”
Visual Homing From Scale With an Uncalibrated Omnidirectional Camera (2013, methodology): “They considered the epipolar geometries as well as the orientation and scale of SIFT features for monocular cameras, following a framework similar to [8].”
Real-time markerless tracking for augmented reality: the virtual visual servoing framework (2006, background): “Essentially, 2D visual servoing [24], [13], [20] consists of specifying a task (mainly, positioning or target tracking tasks) as the regulation in the image of a set of visual features.”
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019, background): “This scenario has application in many real-world problems, such as visual-servoing (Espiau et al., 1992), in which we only observe high-dimensional images of the environment and not its underlying state.”
Control-limited differential dynamic programming (2014, background): “The task-function approach naturally leads to inverse kinematics [3] or operational-space inverse dynamics [4] and is particularly active nowadays in humanoid robotics [5], [6], [7] where it is turned into control machinery by using task sequencing [7].”
Real-Time Visual Tracking of Complex Structures (2002, methodology): “Since the eye-in-hand approach is used, this generates a motion-to-image Jacobian (also known as the interaction screw [ 13 ]) which can be used to generate robot control commands to minimize the image error.”
A new partitioned approach to image-based visual servo control (2001, methodology): “[9] T. S. Shen, J. Huang, and C. H. Menq, " Multiple-sensor integration for rapid and high-precision coordinate metrology, " IEEE/ASME Trans.”
Deep representation of industrial components using simulated images (2017, methodology): “By ﬁnding a set of correspondences between an imaged object and its reference model, we can retrieve objects from a database and compute geometric transformations using the 2D-3D alignment meth-ods [3], [22], [9], [4], [1], [2].”
A bearing-only 2D/3D-homing method under a visual servoing framework (2010, background): “" Visual Servoing " [6] has been widely cited in the area of motion control of industrial robots.”
ViSP for visual servoing: a generic software platform with a wide class of robot control skills (2005, methodology): “For the first point, few systems allow real-time capabilities on a simple workstation.”