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检索条件"机构=Computer Vision and Active Perception Lab Centre for Autonomous Systems"

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Integrated On-line Robot-camera Calibration and Object Pose Estimation

Integrated On-line Robot-camera Calibration and Object Pose ...

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IEEE International Conference on Robotics and Automation

作者： Karl Pauwels Danica Kragic Computer Vision and Active Perception Lab Center for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467380270

We present a novel on-line approach for extrinsic robot-camera calibration, a process often referred to as hand-eye calibration, that uses object pose estimates from a real-time model-based tracking approach. While off-line calibration has seen much progress recently due to the incorporation of bundle adjustment techniques, on-line calibration still remains a largely open problem. Since we update the calibration in each frame, the improvements can be incorporated immediately in the pose estimation itself to facilitate object tracking. Our method does not require the camera to observe the robot or to have markers at certain fixed locations on the robot. To comply with a limited computational budget, it maintains a fixed size configuration set of samples. This set is updated each frame in order to maximize an observability criterion. We show that a set of size 20 is sufficient in real-world scenarios with static and actuated cameras. With this set size, only 100 microseconds are required to update the calibration in each frame, and we typically achieve accurate robot-camera calibration in 10 to 20 seconds. Together, these characteristics enable the incorporation of calibration in normal task execution.

关键词： Calibration Object Pose

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Folding Assembly by Means of Dual-Arm Robotic Manipulation

Folding Assembly by Means of Dual-Arm Robotic Manipulation

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IEEE International Conference on Robotics and Automation

作者： Diogo Almeida Yiannis Karayiannidis Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH SE-100 44 Stockholm Sweden

ISBN: (纸本)9781467380270

In this paper, we consider folding assembly as an assembly primitive suitable for dual-arm robotic assembly, that can be integrated in a higher level assembly strategy. The system composed by two pieces in contact is modelled as an articulated object, connected by a prismatic-revolute joint. Different grasping scenarios were considered in order to model the system, and a simple controller based on feedback linearisation is proposed, using force torque measurements to compute the contact point kinematics. The folding assembly controller has been experimentally tested with two sample parts, in order to showcase folding assembly as a viable assembly primitive.

关键词： Means Dual-Arm Robotic Manipulation system

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Integrating object and grasp recognition for dynamic scene interpretation

Integrating object and grasp recognition for dynamic scene i...

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International Conference on Advanced Robotics (ICAR)

作者： S. Ekvall D. Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Department of Numerical Analysis and Computer Science Royal Institute of Technology Stockholm Sweden

Understanding and interpreting dynamic scenes and activities is a very challenging problem. In this paper, we present a system capable of learning robot tasks from demonstration. Classical robot task programming requires an experienced programmer and a lot of tedious work. In contrast, programming by demonstration is a flexible framework that reduces the complexity of programming robot tasks, and allows end-users to demonstrate the tasks instead of writing code. We present our recent steps towards this goal. A system for learning pick-and-place tasks by manually demonstrating them is presented. Each demonstrated task is described by an abstract model involving a set of simple tasks such as what object is moved, where it is moved, and which grasp type was used to move it

关键词： Layout Robot programming Writing Humans Sensor phenomena and characterization Object recognition computer vision laboratories Numerical analysis computer science

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Monocular real-time 3D articulated hand pose estimation

Monocular real-time 3D articulated hand pose estimation

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IEEE-RAS International Conference on Humanoid Robots

作者： Javier Romero Hedvig Kjellström Danica Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424445875

Markerless, vision based estimation of human hand pose over time is a prerequisite for a number of robotics applications, such as learning by demonstration (LbD), health monitoring, teleoperation, human-robot interaction. It has special interest in humanoid platforms, where the number of degrees of freedom makes conventional programming challenging. Our primary application is LbD in natural environments where the humanoid robot learns how to grasp and manipulate objects by observing a human performing a task. This paper presents a method for continuous vision based estimation of human hand pose. The method is non-parametric, performing a nearest neighbor search in a large database (100000 entries) of hand pose examples. The main contribution is a real time system, robust to partial occlusions and segmentation errors, that provides full hand pose recognition from markerless data. An additional contribution is the modeling of constraints based on temporal consistency in hand pose, without explicitly tracking the hand in the high dimensional pose space. The pose representation is rich enough to enable a descriptive human-to-robot mapping. Experiments show the pose estimation to be more robust and accurate than a non-parametric method without temporal constraints.

关键词： Humanoid robots Real time systems Robustness Human robot interaction Robot vision systems Fingers computer vision computer science Application software computerized monitoring

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In-hand manipulation using gravity and controlled slip

In-hand manipulation using gravity and controlled slip

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： F. E. Vina B Y. Karayiannidis K. Pauwels C. Smith D. Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology KTH SE-100 44 Stockholm Sweden Dept. of Signals and Systems Chalmers University of Technology Gothenburg Sweden The Computer Vision and Active Perception Lab. Royal Institute of Technology Stockholm Sweden

In this work we propose a sliding mode controller for in-hand manipulation that repositions a tool in the robot's hand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch grasp and we model the system as a link attached to the gripper via a passive revolute joint with friction, i.e., the grasp only affords rotational motions of the tool around a given axis of rotation. The robot controls the slippage by varying the opening between the fingers in order to allow the tool to move to the desired angular position following a reference trajectory. We show experimentally how the proposed controller achieves convergence to the desired tool orientation under variations of the tool's inertial parameters.

关键词： Friction Robot kinematics Grippers Torque Gravity

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Generalizing grasps across partly similar objects

Generalizing grasps across partly similar objects

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Justus Piater Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Lab CSC KTH Royal Institute of Technology Stockholm Sweden Intelligent and Interactive Systems group Institute of Computer Science University of Innsbruck Austria

The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

关键词： Grasping Shape Planning Dictionaries Databases Grippers Covariance matrix

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Online Kinematics Estimation for active Human-Robot Manipulation of Jointly Held Objects

Online Kinematics Estimation for Active Human-Robot Manipula...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Yiannis Karayiannidis Christian Smith Francisco E. Vina Danica Kragic Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology (KTH) SE-100 44 Stockholm Sweden.

ISBN: (纸本)9781467363563

This paper introduces a method for estimating the constraints imposed by a human agent on a jointly manipulated object. These estimates can be used to infer knowledge of where the human is grasping an object, enabling the robot to plan trajectories for manipulating the object while subject to the constraints. We describe the method in detail, motivate its validity theoretically, and demonstrate its use in co-manipulation tasks with a real robot.

关键词： constraints object while estimating Constraint Robot kinematics trajectory Does manipulate objects AT-Rich Interactive Domain-Containing Protein 1A

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Obstacle avoidance in formation using navigation-like functions and constraint based programming

Obstacle avoidance in formation using navigation-like functi...

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2013 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Michele Colledanchise Dimos V. Dimarogonas Petter Ögren Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication The Royal Institute of Technology - KTH Stockholm Sweden Centre for Autonomous Systems Automatic Control Lab School of Electrical Engineering The Royal Institute of Technology - KTH Stockholm Sweden

In this paper, we combine navigation function-like potential fields and constraint based programming to achieve obstacle avoidance in formation. Constraint based programming was developed in robotic manipulation as a technique to take several constraints into account when controlling redundant manipulators. The approach has also been generalized, and applied to other control systems such as dual arm manipulators and unmanned aerial vehicles. Navigation functions are an elegant way to design controllers with provable properties for navigation problems. By combining these tools, we take advantage of the redundancy inherent in a multi-agent control problem and are able to concurrently address features such as formation maintenance and goal convergence, even in the presence of moving obstacles. We show how the user can decide a priority ordering of the objectives, as well as a clear way of seeing what objectives are currently addressed and what are postponed. We also analyze the theoretical properties of the proposed controller. Finally, we use a set of simulations to illustrate the approach.

关键词： Navigation Convergence Programming Collision avoidance Robots Maintenance engineering

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Caging Complex Objects with Geodesic Balls

Caging Complex Objects with Geodesic Balls

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Dmitry Zarubin Florian T. Pokorny Marc Toussaint Danica Kragic Machine Learning and Robotics Lab Universitat Stuttgart Stuttgart Germany Centre for Autonomous Systems Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

This paper proposes a novel approach for the synthesis of grasps of objects whose geometry can be observed only in the presence of noise. We focus in particular on the problem of generating caging grasps with a realistic robot hand simulation and show that our method can generate such grasps even on complex objects. We introduce the idea of using geodesic balls on the object's surface in order to approximate the maximal contact surface between a robotic hand and an object. We define two types of heuristics which extract information from approximate geodesic balls in order to identify areas on an object that can likely be used to generate a caging grasp. Our heuristics are based on two scoring functions. The first uses winding angles measuring how much a geodesic ball on the surface winds around a dominant axis, while the second explores using the total discrete Gaussian curvature of a geodesic ball to rank potential caging postures. We evaluate our approach with respect to variations in hand kinematics, for a selection of complex real-world objects and with respect to its robustness to noise.

关键词： Caging Complex Objects Geodesic Balls Gaussian curvature

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Enhanced visual scene understanding through human-robot dialog

Enhanced visual scene understanding through human-robot dial...

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2011 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Matthew Johnson-Roberson Jeannette Bohg Gabriel Skantze Joakim Gustafson Rolf Carlson Babak Rasolzadeh Danica Kragic Centre of Autonomous Systems Computational Vision and Active Perception Laboratory Sweden Department for Speech Music and Hearing Institutes part of School of Computer Science KTH in Stockholm Sweden

We propose a novel human-robot-interaction framework for robust visual scene understanding. Without any a-priori knowledge about the objects, the task of the robot is to correctly enumerate how many of them are in the scene and segment them from the background. Our approach builds on top of state-of-the-art computer vision methods, generating object hypotheses through segmentation. This process is combined with a natural dialog system, thus including a `human in the loop' where, by exploiting the natural conversation of an advanced dialog system, the robot gains knowledge about ambiguous situations. We present an entropy-based system allowing the robot to detect the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

关键词： Robots Entropy Humans Three dimensional displays Histograms Machine vision Image analysis

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