Change search
Link to record
Permanent link

Direct link
BETA
Alternative names
Publications (10 of 71) Show all publications
Butepage, J., Kjellström, H. & Kragic, D. (2018). Anticipating many futures: Online human motion prediction and generation for human-robot interaction. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 4563-4570). IEEE COMPUTER SOC
Open this publication in new window or tab >>Anticipating many futures: Online human motion prediction and generation for human-robot interaction
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE COMPUTER SOC , 2018, p. 4563-4570Conference paper, Published paper (Refereed)
Abstract [en]

Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. The bottleneck of most methods is the lack of an accurate model of natural human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motion patterns. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-237164 (URN)000446394503071 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Swedish Foundation for Strategic Research
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-10-24Bibliographically approved
Hamesse, C., Ackermann, P., Kjellström, H. & Zhang, C. (2018). Simultaneous measurement imputation and outcome prediction for achilles tendon rupture rehabilitation. In: CEUR Workshop Proceedings: . Paper presented at 1st Joint Workshop on AI in Health, AIH 2018, Stockholm, Sweden, 13 July 2018 through 14 July 2018 (pp. 82-86). CEUR-WS, 2142
Open this publication in new window or tab >>Simultaneous measurement imputation and outcome prediction for achilles tendon rupture rehabilitation
2018 (English)In: CEUR Workshop Proceedings, CEUR-WS , 2018, Vol. 2142, p. 82-86Conference paper, Published paper (Refereed)
Abstract [en]

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Accurately predicting the rehabilitation outcome of ATR using noisy measurements with missing entries is crucial for treatment decision support. In this work, we design a probabilistic model that simultaneously predicts the missing measurements and the rehabilitation outcome in an end-to-end manner. We evaluate our model and compare it with multiple baselines including multi-stage methods using an ATR clinical cohort. Experimental results demonstrate the superiority of our model for ATR rehabilitation outcome prediction.

Place, publisher, year, edition, pages
CEUR-WS, 2018
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2142
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-238396 (URN)2-s2.0-85050917241 (Scopus ID)
Conference
1st Joint Workshop on AI in Health, AIH 2018, Stockholm, Sweden, 13 July 2018 through 14 July 2018
Note

QC 20181108

Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2018-11-08Bibliographically approved
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
Show others...
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
Show others...
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
Show others...
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5750-9655

Search in DiVA

Show all publications