Publications - Thematic Area B
Publications project B1 - Neural dynamics in crossmodal prediction
Publications of the current project
Chen, J., Zhang, D., Engel, A. K., Gong, Q., and Maye, A. (2017).
Application of a single-flicker online SSVEP BCI for spatial navigation,
PloS one, 12(5), e0178385.
Maye A, Zhang D, Engel AK (2017).
Utilizing retinotopic mapping for a multi-target SSVEP BCI with a single flicker frequency.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 25: 1026–1036.
Daume J, Graetz S, Gruber T, Engel AK, Friese U (2017).
Cognitive control during audiovisual working memory engages frontotemporal theta-band interactions.
Scientific Reports 7: 12585.
Gudi-Mindermann H, Rimmele JM, Nolte G, Bruns P, Engel AK, Röder B (2018).
Working memory training in congenitally blind individuals results in an integration of occipital cortex of in functional networks.
Behavioral Brain Research: in press.
Relevant previous work
Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2, 704-716.
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013). Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron, 80, 867-886.
Hipp, J. F., Engel, A. K., & Siegel, M. (2011). Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69, 387-396.
Hipp, J. F., Hawellek, D., Corbetta, M., Siegel, M., & Engel, A. K. (2012). Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature Neuroscience, 15, 884-890.
Höfle, M., Pomper, U., Hauck, M., Engel, A. K., & Senkowski, D. (2013). Spectral signatures of viewing a needle approaching one' own body when anticipating pain. European Journal of Neuroscience, 7, 3089-3098.
Hu, X., Sun, C., & Zhang, B. (2010). Design of recurrent neural networks for solving constrained least absolute deviation problems. IEEE Transactions on Neural Networks, 21, 1073-1086.
Liang, M., & Hu, X. (2015). Recurrent convolutional neural network for object recognition. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3367-3375.
Schepers, I. M., Hipp, J. F., Schneider, T. R., Röder, B., & Engel, A. K. (2012). Functionally specific oscillatory activity correlates between visual and auditory cortex in the blind. Brain, 135, 922-934.
Siegel, M., Donner, T. H., Oostenveld, R., Fries, P., & Engel, A. K. (2008). Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron, 60, 709-719.
Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., & Fries, P. (2007). Modulation of neuronal interactions through neuronal synchronization. Science, 316, 1609-1612.
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013). Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron, 80, 867-886.
Hipp, J. F., Engel, A. K., & Siegel, M. (2011). Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69, 387-396.
Hipp, J. F., Hawellek, D., Corbetta, M., Siegel, M., & Engel, A. K. (2012). Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature Neuroscience, 15, 884-890.
Höfle, M., Pomper, U., Hauck, M., Engel, A. K., & Senkowski, D. (2013). Spectral signatures of viewing a needle approaching one' own body when anticipating pain. European Journal of Neuroscience, 7, 3089-3098.
Hu, X., Sun, C., & Zhang, B. (2010). Design of recurrent neural networks for solving constrained least absolute deviation problems. IEEE Transactions on Neural Networks, 21, 1073-1086.
Liang, M., & Hu, X. (2015). Recurrent convolutional neural network for object recognition. Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3367-3375.
Schepers, I. M., Hipp, J. F., Schneider, T. R., Röder, B., & Engel, A. K. (2012). Functionally specific oscillatory activity correlates between visual and auditory cortex in the blind. Brain, 135, 922-934.
Siegel, M., Donner, T. H., Oostenveld, R., Fries, P., & Engel, A. K. (2008). Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron, 60, 709-719.
Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., & Fries, P. (2007). Modulation of neuronal interactions through neuronal synchronization. Science, 316, 1609-1612.
Publications project B2 - Bayesian analysis of the interaction of learning, semantics and social influence
with crossmodal integration
Publications of the current project
Bayer J, Gläscher J, Finsterbusch J, Schulte LH, Sommer T (2018).
Linear and inverted U-shaped dose-response functions describe estrogen effects on hippocampal activity in young women.
Nature Communications, 9, 1220.
Adolphs R, Gläscher J, Tranel D (2018).
Searching for the neural causes of criminal behavior,
PNAS, 115(3), 451-52.
Rusch T, Korn C, Gläscher J (2017).
A two-way street between attention and learning,
Neuron, 93(2), 256-258.
Bogdanov M, Timmermann J, Gläscher J, Hummel F, Schwabe L (in revision).
Causal role of the inferolateral prefrontal cortex in goal-directed control of action,
Scientific Report
Gläscher J, Adolphs R, Tranel D (in revision).
Model-based lesion mapping of the Wisconsin Card Sorting Test,
Nature Communications.
Relevant previous work
Gläscher, J., & Büchel, C. (2005). Formal learning theory dissociates brain regions with different temporal integration. Neuron, 47, 295-306.
Gläscher, J., Hampton, A. N., & O’Doherty, J. (2009). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. Cereb Cortex, 19, 483-495.
Gläscher, J., & O’Doherty, J. (2010). Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. In Sanfey AG, Nadel L (Eds.), Wiley Interdisciplinary Reviews: Cognitive Science, 1, 501-510.
Gläscher, J., Daw, N., Dayan, P., & O’Doherty, J. P. (2010). States versus rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron, 66, 585-595.
Gläscher, J., Adolphs, R., Damasio, H., Bechara, A., Rudrauf, D., Calamia, M., Paul, L. K., & Tranel, D. (2012). Lesion map-ping of cognitive control and value-based decision-making in the prefrontal cortex. Proc Natl Acad Sci USA, 109, 14681-14686.
Chen, N., Zhu, J., Sun, F., & Xing, E. P. (2012). Large-margin Subspace Learning for Multi-view Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34, 2365-2378.
Zhu, J., Ahmed, A., & Xing, E. P. (2012). MedLDA: Maximum Margin Supervised Topic Models. Journal of Machine Learning Research, 13, 2237-2278.
Zhu, J., & Xing, E. P. (2009). Maximum Entropy Discrimination Markov Networks. Journal of Machine Learning Research, 10, 2531-2569.
Zhu, J., Chen, N., & Xing, E. P. (2014a) Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs. Journal of Machine Learning Research, 15, 1799-1847.
Zhu, J., Chen, N., Perkins, H., & Zhang, B. (2014b) Gibbs Max-margin Topic Models with Data Augmentation. Journal of Machine Learning Research, 15, 1073-1110.
Gläscher, J., Hampton, A. N., & O’Doherty, J. (2009). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. Cereb Cortex, 19, 483-495.
Gläscher, J., & O’Doherty, J. (2010). Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. In Sanfey AG, Nadel L (Eds.), Wiley Interdisciplinary Reviews: Cognitive Science, 1, 501-510.
Gläscher, J., Daw, N., Dayan, P., & O’Doherty, J. P. (2010). States versus rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron, 66, 585-595.
Gläscher, J., Adolphs, R., Damasio, H., Bechara, A., Rudrauf, D., Calamia, M., Paul, L. K., & Tranel, D. (2012). Lesion map-ping of cognitive control and value-based decision-making in the prefrontal cortex. Proc Natl Acad Sci USA, 109, 14681-14686.
Chen, N., Zhu, J., Sun, F., & Xing, E. P. (2012). Large-margin Subspace Learning for Multi-view Data Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34, 2365-2378.
Zhu, J., Ahmed, A., & Xing, E. P. (2012). MedLDA: Maximum Margin Supervised Topic Models. Journal of Machine Learning Research, 13, 2237-2278.
Zhu, J., & Xing, E. P. (2009). Maximum Entropy Discrimination Markov Networks. Journal of Machine Learning Research, 10, 2531-2569.
Zhu, J., Chen, N., & Xing, E. P. (2014a) Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs. Journal of Machine Learning Research, 15, 1799-1847.
Zhu, J., Chen, N., Perkins, H., & Zhang, B. (2014b) Gibbs Max-margin Topic Models with Data Augmentation. Journal of Machine Learning Research, 15, 1073-1110.
Publications project B3 - Neurocognitive mechanisms for implicit learning of crossmodal predictions
Publications of the current project
Taesler, P., Jablonowski, J., Fu, Q., Rose, M.,
Modeling Implicit Learning in a Cross-modal Audio-visual Serial Reaction Time Task,
Cognitive Systems Research (2018),
doi: https://doi.org/10.1016/j.cogsys.2018.10.002
Taesler, P. and Rose, M. (2016).
Psychophysically Anchored, Robust Thresholding in Studying Pain Related Lateralization of Oscillatory Pre-Stimulus Activity.
J. Vis. Exp., e55228.
Mareike Clos, Tobias Sommer, Signe L. Schneider, Michael Rose (in Press).
Enhanced transformation of incidentally learned knowledge into explicit memory by dopaminergic modulation;
PLOS One (in Press).
Wu, J., Fu*, Q., Zhou, X., & Sun, X. (2018).
The effect of presenting mode of different features on the acquisition of rule-based
and similarity-based knowledge in category learning.
Journal of Psychological Science, 41(5), 1–6. (in Chinese)
Zhang, M., Fu, Q., Chen, Y-H, & Fu, X. (2018).
Emotional context modulates micro-expression processing as reflected in event-related potentials.
PsyCh Journal, DOI: 10.1002/pchj.196.
Yu, M., Liu, Y. J., Wang, S. J., Fu, Q., Fu, X. (2016).
A PMJ-inspired cognitive framework for natural scene categorization in line drawings.
Neurocomputing, 173, 2041–2048. DOI:10.1016/j.neucom.2015.09.046.
Liu, Y.,Yu, M., Fu, Q., Chen, W., Liu, Y., & Xie, L. (2016).
Cognitive mechanism related to line drawings and its applications in intelligent processing of visual media: A Survey.
Frontiers of Computer Science, 10(2), 216–232.
Fu*, Q., Liu, Y. J., Dienes, Z., Wu, J., Chen, W., & Fu, X. (2017).
Neural correlates of subjective awareness for natural scene categorization of color photographs and line-drawings.
Frontiers in Psychology, 8:210.doi: 10.3389/fpsyg.2017.00210.
Fu*, Q., Sun, H., Dienes, Z., & Fu, X.
Implicit sequence learning of chunking and abstract structures.
Consciousness and Cognition.
Li, K., Fu*, Q., Sun, X., Zhou, X., & Fu, X. (2016).
Paired-associate and feedback-based weather prediction tasks support multiple category learning systems.
Frontiers in Psychology, 7:1017.
doi: http://dx.doi.org/10.3389/fpsyg.2016.01017
Fu*, Q., Liu, Y. J., Dienes, Z., Wu, J., Chen, W., & Fu, X. (2016).
The role of edge-based and surface-based information in natural scene categorization: Evidence from behavior and event-related potentials.
Consciousness and Cognition, 43, 152–166.
doi: http://dx.doi.org/10.1016/j.concog.2016.06.008
Relevant previous work
Fu, Q., Bin G, Dienes, Z., Fu, X., & Gao, X. (2013a). Learning without consciously knowing: Evidence from event-related potentials in sequence learning. Conscious Cogn, 22, 22–34.
Fu, Q., Dienes Z, & Fu, X. (2010). Can unconscious knowledge allow control in sequence learning? Conscious Cogn, 19, 462–474.
Fu, Q., Dienes, Z, Shang, J., & Fu, X. (2013b). Who learns more? Cultural differences in implicit sequence learning. PLoS One, 8, e71625.
Fu, Q., Fu, X., & Dienes, Z. (2008). Implicit sequence learning and conscious awareness. Conscious Cogn, 17, 185–202.
Gao, X., Xu, D., Cheng, M., & Gao, S. (2003). A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehabil Eng, 11, 137-140.
Haider, H., Eberhardt, K., Kunde, A., & Rose, M. (2013). Implicit visual learning and the expression of learning. Conscious Cogn, 22, 82-98.
Rose, M., Haider, H., & Büchel, C. (2010). The emergence of explicit memory during learning. Cerebral Cortex, 12, 2787-2797.
Rose, M., Haider, H., Salari, N. & Büchel, C. (2011). Functional dissociation of hippocampal mechanism during implicit learning based on the domain of associations. The Journal of Neuroscience, 31, 13739-13745.
Rose, M., Haider, H., Weiller, C., & Büchel, C. (2002). The role of medial temporal lobe structures in implicit learning: an event-related fMRI study. Neuron, 36, 1221-1231.
Schmid, C., Büchel, C., & Rose, M. (2011). The neural basis of visual dominance in the context of audio-visual object processing. Neuroimage, 55, 304-311.
Fu, Q., Dienes Z, & Fu, X. (2010). Can unconscious knowledge allow control in sequence learning? Conscious Cogn, 19, 462–474.
Fu, Q., Dienes, Z, Shang, J., & Fu, X. (2013b). Who learns more? Cultural differences in implicit sequence learning. PLoS One, 8, e71625.
Fu, Q., Fu, X., & Dienes, Z. (2008). Implicit sequence learning and conscious awareness. Conscious Cogn, 17, 185–202.
Gao, X., Xu, D., Cheng, M., & Gao, S. (2003). A BCI-based environmental controller for the motion-disabled. IEEE Trans Neural Syst Rehabil Eng, 11, 137-140.
Haider, H., Eberhardt, K., Kunde, A., & Rose, M. (2013). Implicit visual learning and the expression of learning. Conscious Cogn, 22, 82-98.
Rose, M., Haider, H., & Büchel, C. (2010). The emergence of explicit memory during learning. Cerebral Cortex, 12, 2787-2797.
Rose, M., Haider, H., Salari, N. & Büchel, C. (2011). Functional dissociation of hippocampal mechanism during implicit learning based on the domain of associations. The Journal of Neuroscience, 31, 13739-13745.
Rose, M., Haider, H., Weiller, C., & Büchel, C. (2002). The role of medial temporal lobe structures in implicit learning: an event-related fMRI study. Neuron, 36, 1221-1231.
Schmid, C., Büchel, C., & Rose, M. (2011). The neural basis of visual dominance in the context of audio-visual object processing. Neuroimage, 55, 304-311.
Publications project B4 - Brain dynamics of crossmodal learning and conflict processing
Publications of the current project
Relevant previous work
Ewald, A., Marzetti, L., Zappasodi, F., Meinecke, F. C., & Nolte, G. (2012). Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space. NeuroImage, 60(1), 476-488.
Li, Q., Wang, K., Nan, W., Zheng, Y., Wu, H., Wang, H., & Liu, X. (2015). Electrophysiological dynamics reveal distinct processing of stimulus-stimulus and stimulus-response conflicts. Psychophysiology, 52(4), 562-571.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2004). Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI. Neuroimage, 22(3), 1097-1106.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2006). Functional dissociation of attentional selection within PFC: response and non-response related aspects of attentional selection as ascertained by fMRI. Cerebral Cortex, 16(6), 827-834.
Liu, X., Park, Y., Gu, X., & Fan, J. (2010). Dimensional overlap accounts for independence and integration of stimulus—response compatibility effects.Attention, Perception, & Psychophysics, 72(6), 1710-1720.
Luft, C. D. B., Nolte, G., & Bhattacharya, J. (2013). High-learners present larger mid-frontal theta power and connectivity in response to incorrect performance feedback. The Journal of Neuroscience, 33(5), 2029-2038.
Nolte, G. (2003). The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Physics in medicine and biology, 48(22), 3637.
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical neurophysiology, 115(10), 2292-2307.
Nolte, G., Ziehe, A., Nikulin, V. V., Schlögl, A., Krämer, N., Brismar, T., & Müller, K. R. (2008). Robustly estimating the flow direction of information in complex physical systems. Physical review letters, 100(23), 234101.
Wang, K., Li, Q., Zheng, Y., Wang, H., & Liu, X. (2014). Temporal and spectral profiles of stimulus–stimulus and stimulus–response conflict processing. Neuroimage, 89, 280-288.
Li, Q., Wang, K., Nan, W., Zheng, Y., Wu, H., Wang, H., & Liu, X. (2015). Electrophysiological dynamics reveal distinct processing of stimulus-stimulus and stimulus-response conflicts. Psychophysiology, 52(4), 562-571.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2004). Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI. Neuroimage, 22(3), 1097-1106.
Liu, X., Banich, M. T., Jacobson, B. L., & Tanabe, J. L. (2006). Functional dissociation of attentional selection within PFC: response and non-response related aspects of attentional selection as ascertained by fMRI. Cerebral Cortex, 16(6), 827-834.
Liu, X., Park, Y., Gu, X., & Fan, J. (2010). Dimensional overlap accounts for independence and integration of stimulus—response compatibility effects.Attention, Perception, & Psychophysics, 72(6), 1710-1720.
Luft, C. D. B., Nolte, G., & Bhattacharya, J. (2013). High-learners present larger mid-frontal theta power and connectivity in response to incorrect performance feedback. The Journal of Neuroscience, 33(5), 2029-2038.
Nolte, G. (2003). The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. Physics in medicine and biology, 48(22), 3637.
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical neurophysiology, 115(10), 2292-2307.
Nolte, G., Ziehe, A., Nikulin, V. V., Schlögl, A., Krämer, N., Brismar, T., & Müller, K. R. (2008). Robustly estimating the flow direction of information in complex physical systems. Physical review letters, 100(23), 234101.
Wang, K., Li, Q., Zheng, Y., Wang, H., & Liu, X. (2014). Temporal and spectral profiles of stimulus–stimulus and stimulus–response conflict processing. Neuroimage, 89, 280-288.
Publications project B5 - Crossmodal fusion for dexterous manipulation in proactive human-robot collaboration
Publications of the current project
Gao, G., Lauri, M., Zhang, J., and Frintrop, S. (2018).
Occlusion Resistant Object Rotation Regression from Point Cloud Segments,
Proceedings of the ECCV workshop on Recovering 6D Object Pose, 2018.
https://arxiv.org/abs/1808.05498.
Liang, H., Li, S., Görner, M., and Zhang, J. (2018).
Generating Robust Grasps for Unknown Objects in Clutter Using Point Cloud Data.
Shanghai International Symposium on Human-Centered Robotics (HCR), 299-303, 2018.
Liang, H., and Zhao, Q. (2018).
Multi-View CNNs for 3D Hand Pose Estimation,
Shanghai International Symposium on Human-Centered Robotics (HCR), 255-259, 2018.
Sebastian Starke, Norman Hendrich, and Jianwei Zhang (2018).
Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation,
IEEE Transactions on Evolutionary Computation, 2018.
doi: 10.1109/TEVC.2018.2867601
Fuchun Sun, Chunfang Liu, Wenbing, Jianwei Zhang. (2016).
Object classification and grasp planning using visual and tactile sensing,
IEEE Transactions on Systems Man Cybernetics-Systems, 2016.7.1, 46(7): 969~979
Tao Kong, Fuchun Sun, et al. (2018).
Deep Feature Pyramid Reconfiguration for Object Dectection.
ECCV, 2018.
Fuchun Sun, Wenchang Zhang, et al. (2018).
Fused Fuzzy Petri Nets: A Shared Control Method for Brain Computer Interface Systems,
IEEE Trans. On Cognitive and Developmental Systems, 2018.
Wenbing Huang, Fuchun Sun, et al. (2016).
Sparse coding and dictionary learning with learning dynamical systems,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.6.26-2016.7.1
Huaping Liu, Di Guo, Fuchun Sun. (2016).
Object recognition using tactile measurements: Kernel sparse coding methods,
IEEE Transactions on Instrumentation and Measurement, vol.65, no.3, pp.656-665, 2016
Bin Fang, Fuchun Sun. (2016).
A Glove-based system for object recognition via Visual-Tactile Fusion.
ELM2016.
(Best paper award)
Tao Kong, Fuchun Sun, (2017).
RON: Reverse Connection with Objectness Prior Networks for Object Detection,
CVPR 2017.
Jing M, Ma X, Sun F, et al. (2018).
Learning and Inference Movement with Deep Generative Model.
arXiv preprint arXiv:1805.07252, 2018.
Ma X, Jing M, Sun F, et al. (2018).
Adversarial Task Transfer from Preference. arXiv preprint arXiv:1805.04686, 2018
Huaping Liu, Fuchun Sun, Di Guo, Bin Fang, (2017).
Structured output-associated dictionary learning for haptic understanding,
IEEE Transactions on Systems, Man and Cybernetics: Systems, vol.47, no.7, 2017, pp.1564-1574
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Ruppel, P., Jonetzko, J., Görner, M., Hendrich, N., Zhang, J. (2018).
Simulation of the SynTouch BioTac Sensor,
The 15th International Conference on Intelligent Autonomous Systems, (IAS-15 2018), Baden Baden, Germany.
Ruppel, P., Hendrich, N., Starke, S., Zhang, J.
(2018).
Cost Functions to Specify Full-Body Motion and Multi-Goal Manipulation Tasks,
IEEE International Conference on Robotics and Automation, (ICRA-2018), Brisbane, Australia.
Fang, B., Sun, F., Yang, C., Xue, H., Chen, W., Zhang, C., Guo, D., Liu, H.
(2018).
A Dual-Modal Vision-based Tactile Sensor for Robotic Hand Grasping,
IEEE International Conference on Robotics and Automation, (ICRA-2018), Brisbane, Australia.
Deng, Z., Guan, H., Huang, R., Liang, H., Zhang, L., Zhang, J. (2017).
Combining Model-based Q-learning with Structural Knowledge Transfer for Robot Skill Learning.
IEEE Transactions on Cognitive and Developmental Systems.
Han, D., Nie, H., Chen, J., Chen, M., Deng, Z., Zhang, J. (2018).
Multi-modal haptic image recognition based on deep learning.
Sensor Review.
Huang, T., Sun, F., Fang, B., (2017).
A novel wearable tactile sensor for fingertip motion recognition.
IEEE Intl. Conference on Robotics and Biomimetics, (ROBIO-2017), Macau, China.
Li, X., Sun, F., Fang, B.,(2017).
Learning to detect slip for stable grasping.
IEEE Intl. Conference on Robotics and Biomimetics, (ROBIO-2017), Macau, China.
Wei, X., Sun, F., Fang, B., (2017).
Robotic skills learning using dynamical movement primitives from a wearable device.
IEEE Intl. Conference on Robotics and Biomimetics, (ROBIO-2017), Macau, China.
Bestmann, M., Wasserfall, F., Hendrich, N., Zhang, J. (2017).
Replacing Cables on Robotic Arms by Using Serial via Bluetooth,
IEEE Intl. Conference on Robotics and Biomimetics, (ROBIO-2017), Macau, China.
DOI: 10.1109/ROBIO.2017.8324416
Starke, S., Hendrich, N., Zhang, J. (2017).
Multi-Objective Evolutionary Optimisation for Inverse Kinematics on Highly Articulated and Humanoid Robots,
IEEE Intl. Conference on Intelligent Robots and Systems, (IROS-2017), Vancouver, Canada.
DOI: 10.1109/IROS.2017.8206620
Wasserfall, F., Hendrich, N., Fiedler, F., Zhang, J. (2017).
3D-Printed Low-Cost Modular Force Sensors,
20th Intl. Conference on Climbing and Walking Robots (CLAWAR-2017).
In "Human-Centric Robotics", ISBN: 978-981-3231-03-0 (hardcover). ISBN: 978-981-3231-05-4 (ebook).
Wasserfall, F., Hendrich, N., Zhang, J. (2017).
Adaptive Slicing for the FDM Process Revisited,
13th IEEE Conference on Automation Science and Engineering (CASE-2017).
DOI: 10.1109/COASE.2017.8256074
Starke, S., Hendrich, N., Zhang, J. (2017).
A Memetic Evolutionary Algorithm for Real-Time Articulated Kinematic Motion,
IEEE Congress on Evolutionary Computation (CEC 2017), p 2473-2479, Sán Sebastian, Spain, 2017.
DOI: 10.1109/CEC.2017.7969605
Starke, S., Hendrich, N., Zhang, J. (2017).
A Forward Kinematics Data Structure for Efficient Evolutionary Inverse Kinematics,
in S. Zeghloul et al. (eds.), Computational Kinematics, Mechanisms and Machine Science 50, Springer 2018.
DOI: 10.1007/978-3-319-60867-9_64
Albrecht, S., Görner, M., Hertzberg, J., Zhang, J. (2016).
Autonomous Transparent Object Reconstruction,
DGR Days (Deutsche Gesellschaft für Robotik), Leipzig, Germany, 2016.
Starke, S., Hendrich, N., Magg, S., Zhang, J. (2016).
An Efficient Hybridization of Genetic Algorithms and Particle Swarm Optimization for Inverse Kinematics,
IEEE Intl. Conference on Robotics and Biomimetics (ROBIO 2016), 3-7 Dec. 2016, Qingdao, China.
DOI: 10.1109/ROBIO.2016.7866587
Deng, Z., Mi, J., Chen, Z., Einig, L., Zou, C., Zhang, J. (2016).
Learning human compliant behavior from demonstration for force-based robot manipulation,
IEEE Intl. Conference on Robotics and Biomimetics (ROBIO 2016),
3-7 Dec. 2016, Qingdao, China.
DOI: 10.1109/ROBIO.2016.7866342
He, J., Pu, S., Zhang, J. (2016).
In-hand Manipulation with Fixed and Spring Support Fingers,
'Third International Conference on Cognitive Systems and Information Processing (ICCSIP2016),
Beijing, 2016.
Relevant previous work
Bernardino, A., Henriques, M., Hendrich, N., & Zhang, J. (2013, December). Precision grasp synergies for dexterous robotic hands. In Proc. IEEE Conference on Robotics and Biomimetics, ROBIO 2013 (pp. 62-67). IEEE. (Best Conference Paper Award).
Chen, N., Zhu, J., Sun, F., & Xing, E. P. (2012). Large-margin predictive latent subspace learning for multiview data analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(12), 2365-2378.
Cheng, G., Hendrich, N., & Zhang, J. (2012, October). Action gist based automatic segmentation for periodic in-hand manipulation movement learning. In Proc. IEEE Conference on Intelligent Robots and Systems, IROS 2012 pp. 4768-4775). IEEE.
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