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38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016

作者： Bütepage, Judith Kjellström, Hedvig Kragic, Danica Computer Vision and Active Perception Laboratory CSC KTH Stockholm Sweden

ISBN: (纸本)9780991196739

False-belief task have mainly been associated with the explanatory notion of the theory of mind and the theory-theory. However, it has often been pointed out that this kind of high-level reasoning is computational and time expensive. During the last decades, the idea of embodied intelligence, i.e. complex behavior caused by sensorimotor contingencies, has emerged in both the fields of neuroscience, psychology and artificial intelligence. Viewed from this perspective, the failing in a false-belief test can be the result of the impairment to recognize and track others' sensorimotor contingencies and affordances. Thus, social cognition is explained in terms of low-level signals instead of high-level reasoning. In this work, we present a generative model for optimal action selection which simultaneously can be employed to make predictions of others' actions. As we base the decision making on a hidden state representation of sensorimotor signals, this model is in line with the ideas of embodied intelligence. We demonstrate how the tracking of others' hidden states can give rise to correct false-belief inferences, while a lack thereof leads to failing. With this work, we want to emphasize the importance of sensorimotor contingencies in social cognition, which might be a key to artificial, socially intelligent systems. © 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.

关键词： Decision making

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RCAMP: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity

arXiv

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arXiv 2017年

作者： Caccamo, Sergio Parasuraman, Ramviyas Freda, Luigi Gianni, Mario Ögren, Petter Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Sweden Purdue University West Lafayette United States ALCOR Laboratory DIAG Sapienza University of Rome Italy

Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios. Copyright © 2017, The Authors. All rights reserved.

关键词： Mobile robots

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Topology, Big Data and Optimization

Studies in Big Data

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Studies in Big Data 2016年 18卷 147-176页

作者： Vejdemo-Johansson, Mikael Skraba, Primoz Computer Vision and Active Perception Laboratory KTH Royal Institute of Technology Stockholm100 44 Sweden AI Laboratory Jozef Stefan Institute Jamova 39 Ljubljana Slovenia

The idea of using geometry in learning and inference has a long history going back to canonical ideas such as Fisher information, Discriminant analysis, and Principal component analysis. The related area of Topological Data Analysis (TDA) has been developing in the last decade. The idea is to extract robust topological features from data and use these summaries for modeling the data. A topological summary generates a coordinate-free, deformation invariant and highly compressed description of the geometry of an arbitrary data set. Topological techniques are well-suited to extend our understanding of Big Data. These tools do not supplant existing techniques, but rather provide a complementary viewpoint to existing techniques. The qualitative nature of topological features do not give particular importance to individual samples, and the coordinate-free nature of topology generates algorithms and viewpoints well suited to highly complex datasets. With the introduction of persistence and other geometric-topological ideas we can find and quantify local-to-global properties as well as quantifying qualitative changes in data. © Springer International Publishing Switzerland 2016.

关键词： Big data

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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

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IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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Estimating Attention in Exhibitions Using Wearable Cameras

Estimating Attention in Exhibitions Using Wearable Cameras

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International Conference on Pattern Recognition

作者： Ali S. Razavian Omid Aghazadeh Josephine Sullivan Stefan Carlsson KTH Computer Vision and Active Perception Laboratory Sweden

ISBN: (纸本)9781479952106

This paper demonstrates a system for automatic detection of visual attention and identification of salient items at exhibitions (e.g. museum or an auction). The method is offline and is done on a video captured by a head mounted camera. Towards the estimation of attention, we define the notions of "saliency" and "interestingness" for an exhibition items. Our method is a combination of multiple state of the art techniques from different vision tasks such as tracking, image matching and retrieval. Many experiments are conducted to evaluate multiple aspects of our method. The method has proven to be robust to image blur, occlusion, truncation, and dimness. The experiments shows strong performance for the tasks of matching items, estimating focus frames and detecting salient and interesting items. This can be useful to the commercial vendors and museum curators and help them to understand which items are appealing more to the visitors.

关键词： Cameras Vectors Head Support vector machines Robustness Feature extraction Optical imaging

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Model-aided State Estimation for Quadrotor Micro Air Vehicles amidst Wind Disturbances

Model-aided State Estimation for Quadrotor Micro Air Vehicle...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Dinuka Abeywardena Zhan Wang Gamini Dissanayake Steven L. Waslander Sarath Kodagoda Centre for Autonomous Systems University of Technology Sydney Computer Vision and Active Perception Lab Centre for Autonomous Systems CSC The Royal Institute of Technology KTH Stockholm 100 44 Sweden Waterloo Autonomous Vehicles Laboratory University of Waterloo Waterloo ON Canada N2L 3G1

ISBN: (纸本)9781479969357

This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims.

关键词： Mathematical model Wind speed Equations Observability Accelerometers Vectors Vehicle dynamics

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A probabilistic framework for task-oriented grasp stability assessment

A probabilistic framework for task-oriented grasp stability ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yasemin Bekiroglu Dan Song Lu Wang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.

关键词： Robot sensing systems Bayes methods Stability analysis Probabilistic logic Planning Grasping

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Learning a dictionary of prototypical grasp-predicting parts from grasping experience

Learning a dictionary of prototypical grasp-predicting parts...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

关键词： Robots Shape Prototypes Three-dimensional displays Grasping Dictionaries Training

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Sparse summarization of robotic grasping data

Sparse summarization of robotic grasping data

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Martin Hjelm Carl Henrik Ek Renaud Detry Hedvig Kjellström Danica Kragic Centre for Autonomous Systems Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

关键词： Robots Data models Grasping Shape Kernel Optimization Encoding

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Model-free robot manipulation of doors and drawers by means of fixed-grasps

Model-free robot manipulation of doors and drawers by means ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yiannis Karayiannidis Christian Smith Francisco E. Viña Petter Ögren Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden

This paper addresses the problem of robot interaction with objects attached to the environment through joints such as doors or drawers. We propose a methodology that requires no prior knowledge of the objects' kinematics, including the type of joint - either prismatic or revolute. The method consists of a velocity controller which relies on force/torque measurements and estimation of the motion direction, rotational axis and the distance from the center of rotation. The method is suitable for any velocity controlled manipulator with a force/torque sensor at the end-effector. The force/torque control regulates the applied forces and torques within given constraints, while the velocity controller ensures that the end-effector moves with a task-related desired tangential velocity. The paper also provides a proof that the estimates converge to the actual values. The method is evaluated in different scenarios typically met in a household environment.

关键词： Torque Force Robots Hardware

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