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Publications (10 of 69) Show all publications
Karipidou, K., Ahnlund, J., Friberg, A., Alexanderson, S. & Kjellström, H. (2017). Computer Analysis of Sentiment Interpretation in Musical Conducting. In: Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017: . Paper presented at 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, Washington, United States, 30 May 2017 through 3 June 2017 (pp. 400-405). IEEE, Article ID 7961769.
Open this publication in new window or tab >>Computer Analysis of Sentiment Interpretation in Musical Conducting
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2017 (English)In: Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, IEEE, 2017, p. 400-405, article id 7961769Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a unique dataset consisting of 20 recordings of the same musical piece, conducted with 4 different musical intentions in mind. The upper body and baton motion of a professional conductor was recorded, as well as the sound of each instrument in a professional string quartet following the conductor. The dataset is made available for benchmarking of motion recognition algorithms. An HMM-based emotion intent classification method is trained with subsets of the data, and classification of other subsets of the data show firstly that the motion of the baton communicates energetic intention to a high degree, secondly, that the conductor’s torso, head and other arm conveys calm intention to a high degree, and thirdly, that positive vs negative sentiments are communicated to a high degree through other channels than the body and baton motion – most probably, through facial expression and muscle tension conveyed through articulated hand and finger motion. The long-term goal of this work is to develop a computer model of the entire conductor-orchestra communication pro- cess; the studies presented here indicate that computer modeling of the conductor-orchestra communication is feasible.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Computer and Information Sciences
Research subject
Speech and Music Communication
Identifiers
urn:nbn:se:kth:diva-208886 (URN)10.1109/FG.2017.57 (DOI)000414287400054 ()2-s2.0-85026288976 (Scopus ID)9781509040230 (ISBN)
Conference
12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017, Washington, United States, 30 May 2017 through 3 June 2017
Note

QC 20170616

Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2018-09-13Bibliographically approved
Zhang, C., Kjellström, H. & Mandt, S. (2017). Determinantal point processes for mini-batch diversification. In: Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017: . Paper presented at 33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, 11 August 2017 through 15 August 2017. AUAI Press Corvallis
Open this publication in new window or tab >>Determinantal point processes for mini-batch diversification
2017 (English)In: Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017, AUAI Press Corvallis , 2017Conference paper (Refereed)
Abstract [en]

We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and gives low probabilities to mini-batches which contain redundant data, and higher probabilities to mini-batches with more diverse data. This simultaneously balances the data and leads to stochastic gradients with lower variance. We term this approach Diversified Mini-Batch SGD (DM-SGD). We show that regular SGD and a biased version of stratified sampling emerge as special cases. Furthermore, DM-SGD generalizes stratified sampling to cases where no discrete features exist to bin the data into groups. We show experimentally that our method results more interpretable and diverse features in unsupervised setups, and in better classification accuracies in supervised setups.

Place, publisher, year, edition, pages
AUAI Press Corvallis, 2017
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-218565 (URN)2-s2.0-85031095282 (Scopus ID)
Conference
33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, 11 August 2017 through 15 August 2017
Note

QC 20171129

Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2017-11-29Bibliographically approved
Zhang, Y., Beskow, J. & Kjellström, H. (2017). Look but Don’t Stare: Mutual Gaze Interaction in Social Robots. In: 9th International Conference on Social Robotics, ICSR 2017: . Paper presented at 9th International Conference on Social Robotics, ICSR 2017, Tsukuba, Japan, 22 November 2017 through 24 November 2017 (pp. 556-566). Springer, 10652
Open this publication in new window or tab >>Look but Don’t Stare: Mutual Gaze Interaction in Social Robots
2017 (English)In: 9th International Conference on Social Robotics, ICSR 2017, Springer, 2017, Vol. 10652, p. 556-566Conference paper, Published paper (Refereed)
Abstract [en]

Mutual gaze is a powerful cue for communicating social attention and intention. A plethora of studies have demonstrated the fundamental roles of mutual gaze in establishing communicative links between humans, and enabling non-verbal communication of social attention and intention. The amount of mutual gaze between two partners regulates human-human interaction and is a sign of social engagement. This paper investigates whether implementing mutual gaze in robotic systems can achieve social effects, thus to improve human robot interaction. Based on insights from existing human face-to-face interaction studies, we implemented an interactive mutual gaze model in an embodied agent, the social robot head Furhat. We evaluated the mutual gaze prototype with 24 participants in three applications. Our results show that our mutual gaze model improves social connectedness between robots and users.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10652
National Category
Interaction Technologies
Identifiers
urn:nbn:se:kth:diva-219664 (URN)10.1007/978-3-319-70022-9_55 (DOI)2-s2.0-85035749029 (Scopus ID)9783319700212 (ISBN)
Conference
9th International Conference on Social Robotics, ICSR 2017, Tsukuba, Japan, 22 November 2017 through 24 November 2017
Note

QC 20171211

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-05-24Bibliographically approved
Caccamo, S., Güler, P., Kjellström, H. & Kragic, D. (2016). Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics. In: IEEE-RAS International Conference on Humanoid Robots: . Paper presented at 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, 15 November 2016 through 17 November 2016 (pp. 530-537). IEEE
Open this publication in new window or tab >>Active perception and modeling of deformable surfaces using Gaussian processes and position-based dynamics
2016 (English)In: IEEE-RAS International Conference on Humanoid Robots, IEEE, 2016, p. 530-537Conference paper, Published paper (Refereed)
Abstract [en]

Exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters. The most common approaches model deformable properties from sets of offline observations using computationally expensive force-based simulators. In this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions. The method takes advantage of Gaussian Processes for constructing a model of the environment geometry surrounding a robot. A fast Position-based Dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment. Gaussian Process Regression maps the local deformability on the whole environment in order to generate a deformability distribution map. We show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm and an Optoforce sensor on different deformable surfaces.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Active perception, Deformability modeling, Gaussian process, Position-based dynamics, Tactile exploration, Anthropomorphic robots, Deformation, Dynamics, Gaussian noise (electronic), Probability distributions, Robots, Active perceptions, Environmental observation, Gaussian process regression, Gaussian Processes, Multiple interactions, Physical interactions, Probabilistic framework, Gaussian distribution
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-202842 (URN)10.1109/HUMANOIDS.2016.7803326 (DOI)000403009300081 ()2-s2.0-85010190205 (Scopus ID)9781509047185 (ISBN)
Conference
16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, 15 November 2016 through 17 November 2016
Note

QC 20170317

Available from: 2017-03-17 Created: 2017-03-17 Last updated: 2018-04-11Bibliographically approved
Qu, A., Zhang, C., Ackermann, P. & Kjellström, H. (2016). Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation. In: : . Paper presented at NIPS Workshop on Machine Learning for Health.
Open this publication in new window or tab >>Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation
2016 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-197302 (URN)
Conference
NIPS Workshop on Machine Learning for Health
Note

QC 20161208

Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2016-12-08Bibliographically approved
Pieropan, A., Bergstroem, N., Ishikawa, M. & Kjellström, H. (2016). Robust and adaptive keypoint-based object tracking. Advanced Robotics, 30(4), 258-269
Open this publication in new window or tab >>Robust and adaptive keypoint-based object tracking
2016 (English)In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 30, no 4, p. 258-269Article in journal (Refereed) Published
Abstract [en]

Object tracking is a fundamental ability for a robot; manipulation as well as activity recognition relies on the robot being able to follow objects in the scene. This paper presents a tracker that adapts to changes in object appearance and is able to re-discover an object that was lost. At its core is a keypoint-based method that exploits the rigidity assumption: pairs of keypoints maintain the same relations over similarity transforms. Using a structured approach to learning, it is able to incorporate new appearances in its model for increased robustness. We show through quantitative and qualitative experiments the benefits of the proposed approach compared to the state of the art, even for objects that do not strictly follow the rigidity assumption.

Place, publisher, year, edition, pages
Robotics Society of Japan, 2016
Keywords
learning, Object tracking, real-time tracker, pose estimation, keypoints
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-185078 (URN)10.1080/01691864.2015.1129360 (DOI)000372182900003 ()2-s2.0-84960959205 (Scopus ID)
Note

QC 20160414

Available from: 2016-04-14 Created: 2016-04-11 Last updated: 2018-01-10Bibliographically approved
Pieropan, A., Bergström, N., Ishikawa, M., Kragic, D. & Kjellström, H. (2016). Robust tracking of unknown objects through adaptive size estimation and appearance learning. In: Proceedings - IEEE International Conference on Robotics and Automation: . Paper presented at 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, 16 May 2016 through 21 May 2016 (pp. 559-566). IEEE conference proceedings
Open this publication in new window or tab >>Robust tracking of unknown objects through adaptive size estimation and appearance learning
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2016 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2016, p. 559-566Conference paper, Published paper (Refereed)
Abstract [en]

This work employs an adaptive learning mechanism to perform tracking of an unknown object through RGBD cameras. We extend our previous framework to robustly track a wider range of arbitrarily shaped objects by adapting the model to the measured object size. The size is estimated as the object undergoes motion, which is done by fitting an inscribed cuboid to the measurements. The region spanned by this cuboid is used during tracking, to determine whether or not new measurements should be added to the object model. In our experiments we test our tracker with a set of objects of arbitrary shape and we show the benefit of the proposed model due to its ability to adapt to the object shape which leads to more robust tracking results.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keywords
Adaptive learning mechanism, Appearance learning, Arbitrary shape, Object model, Rgb-d cameras, Robust tracking, Size estimation, Unknown objects, Robotics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-197233 (URN)10.1109/ICRA.2016.7487179 (DOI)000389516200070 ()2-s2.0-84977519696 (Scopus ID)9781467380263 (ISBN)
Conference
2016 IEEE International Conference on Robotics and Automation, ICRA 2016, 16 May 2016 through 21 May 2016
Note

QC 20161207

Available from: 2016-12-07 Created: 2016-11-30 Last updated: 2018-01-13Bibliographically approved
Güler, R., Pauwels, K., Pieropan, A., Kjellström, H. & Kragic, D. (2015). Estimating the Deformability of Elastic Materials using Optical Flow and Position-based Dynamics. In: Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on: . Paper presented at IEEE-RAS International Conference on Humanoid Robots, November 3-5, KIST, Seoul, Korea (pp. 965-971). IEEE conference proceedings
Open this publication in new window or tab >>Estimating the Deformability of Elastic Materials using Optical Flow and Position-based Dynamics
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2015 (English)In: Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on, IEEE conference proceedings, 2015, p. 965-971Conference paper, Published paper (Refereed)
Abstract [en]

Knowledge of the physical properties of objects is essential in a wide range of robotic manipulation scenarios. A robot may not always be aware of such properties prior to interaction. If an object is incorrectly assumed to be rigid, it may exhibit unpredictable behavior when grasped. In this paper, we use vision based observation of the behavior of an object a robot is interacting with and use it as the basis for estimation of its elastic deformability. This is estimated in a local region around the interaction point using a physics simulator. We use optical flow to estimate the parameters of a position-based dynamics simulation using meshless shape matching (MSM). MSM has been widely used in computer graphics due to its computational efficiency, which is also important for closed-loop control in robotics. In a controlled experiment we demonstrate that our method can qualitatively estimate the physical properties of objects with different degrees of deformability.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-175162 (URN)10.1109/HUMANOIDS.2015.7363486 (DOI)000377954900145 ()2-s2.0-84962249847 (Scopus ID)
Conference
IEEE-RAS International Conference on Humanoid Robots, November 3-5, KIST, Seoul, Korea
Note

QC 20160217

Available from: 2015-10-09 Created: 2015-10-09 Last updated: 2018-01-11Bibliographically approved
Pieropan, A., Bergström, N., Ishikawa, M. & Kjellström, H. (2015). Robust 3D tracking of unknown objects. In: Proceedings - IEEE International Conference on Robotics and Automation: . Paper presented at 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, 26 May 2015 through 30 May 2015 (pp. 2410-2417). IEEE conference proceedings (June)
Open this publication in new window or tab >>Robust 3D tracking of unknown objects
2015 (English)In: Proceedings - IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2015, no June, p. 2410-2417Conference paper, Published paper (Refereed)
Abstract [en]

Visual tracking of unknown objects is an essential task in robotic perception, of importance to a wide range of applications. In the general scenario, the robot has no full 3D model of the object beforehand, just the partial view of the object visible in the first video frame. A tracker with this information only will inevitably lose track of the object after occlusions or large out-of-plane rotations. The way to overcome this is to incrementally learn the appearances of new views of the object. However, this bootstrapping approach is sensitive to drifting due to occasional inclusion of the background into the model. In this paper we propose a method that exploits 3D point coherence between views to overcome the risk of learning the background, by only learning the appearances at the faces of an inscribed cuboid. This is closely related to the popular idea of 2D object tracking using bounding boxes, with the additional benefit of recovering the full 3D pose of the object as well as learning its full appearance from all viewpoints. We show quantitatively that the use of an inscribed cuboid to guide the learning leads to significantly more robust tracking than with other state-of-the-art methods. We show that our tracker is able to cope with 360 degree out-of-plane rotation, large occlusion and fast motion.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keywords
Tracking (position), Fast motions, Large occlusion, Out-of-plane rotation, Partial views, Robust tracking, State-of-the-art methods, Unknown objects, Visual Tracking, Robotics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-176136 (URN)10.1109/ICRA.2015.7139520 (DOI)000370974902060 ()2-s2.0-84938249572 (Scopus ID)
Conference
2015 IEEE International Conference on Robotics and Automation, ICRA 2015, 26 May 2015 through 30 May 2015
Note

QC 20151202. QC 20160411

Available from: 2015-12-02 Created: 2015-11-02 Last updated: 2016-04-11Bibliographically approved
Pieropan, A., Salvi, G., Pauwels, K. & Kjellström, H. (2014). Audio-Visual Classification and Detection of Human Manipulation Actions. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014): . Paper presented at 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Palmer House Hilton Hotel Chicago, United States, 14 September 2014 through 18 September 2014 (pp. 3045-3052). IEEE conference proceedings
Open this publication in new window or tab >>Audio-Visual Classification and Detection of Human Manipulation Actions
2014 (English)In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE conference proceedings, 2014, p. 3045-3052Conference paper, Published paper (Refereed)
Abstract [en]

Humans are able to merge information from multiple perceptional modalities and formulate a coherent representation of the world. Our thesis is that robots need to do the same in order to operate robustly and autonomously in an unstructured environment. It has also been shown in several fields that multiple sources of information can complement each other, overcoming the limitations of a single perceptual modality. Hence, in this paper we introduce a data set of actions that includes both visual data (RGB-D video and 6DOF object pose estimation) and acoustic data. We also propose a method for recognizing and segmenting actions from continuous audio-visual data. The proposed method is employed for extensive evaluation of the descriptive power of the two modalities, and we discuss how they can be used jointly to infer a coherent interpretation of the recorded action.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keywords
Acoustic data, Audio-visual, Audio-visual data, Coherent representations, Human manipulation, Multiple source, Unstructured environments, Visual data
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-158004 (URN)10.1109/IROS.2014.6942983 (DOI)000349834603023 ()2-s2.0-84911478073 (Scopus ID)978-1-4799-6934-0 (ISBN)
Conference
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Palmer House Hilton Hotel Chicago, United States, 14 September 2014 through 18 September 2014
Note

QC 20150122

Available from: 2014-12-18 Created: 2014-12-18 Last updated: 2018-01-11Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5750-9655

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