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Patrick van der Smagt
P. Patrick van der Smagt
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- affiliation: German Aerospace Center, Germany
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2020 – today
- 2024
- [j24]Elie Aljalbout
, Felix Frank
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer. IEEE Robotics Autom. Lett. 9(6): 5895-5902 (2024) - [c67]Nutan Chen, Botond Cseke, Elie Aljalbout, Alexandros Paraschos, Marvin Alles, Patrick van der Smagt:
Guided Decoding for Robot On-line Motion Generation and Adaption. Humanoids 2024: 321-327 - [c66]Nikolas J. Wilhelm, Sami Haddadin, Rainer Burgkart, Patrick van der Smagt, Maximilian Karl:
Accurate Kinematic Modeling using Autoencoders on Differentiable Joints. ICRA 2024: 7122-7128 - [c65]Nikolas J. Wilhelm, Claudio Glowalla, Sami Haddadin, Julian Schote, Hannes Höppner, Patrick van der Smagt, Maximilian Karl, Rainer Burgkart:
Design and Implementation of a Robotic Testbench for Analyzing Pincer Grip Execution in Human Specimen Hands. ICRA 2024: 18465-18471 - [i53]Yin Li, Yu Xiong, Wenxin Fan, Kai Wang, Qingqing Yu, Liping Si, Patrick van der Smagt
Sequential Model for Predicting Patient Adherence in Subcutaneous Immunotherapy for Allergic Rhinitis. CoRR abs/2401.11447 (2024) - [i52]Nutan Chen, Elie Aljalbout, Botond Cseke, Patrick van der Smagt
Guided Decoding for Robot Motion Generation and Adaption. CoRR abs/2403.15239 (2024) - [i51]Yin Li, Qi Chen, Kai Wang, Meige Li, Liping Si, Yingwei Guo, Yu Xiong, Qixing Wang, Yang Qin, Ling Xu, Patrick van der Smagt, Jun Tang, Nutan Chen:
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation. CoRR abs/2404.03253 (2024) - [i50]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models. CoRR abs/2404.18896 (2024) - [i49]Djalel Benbouzid, Christiane Plociennik, Laura Lucaj, Mihai Maftei
Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems. CoRR abs/2405.13191 (2024) - [i48]Elie Aljalbout, Felix Frank, Patrick van der Smagt, Alexandros Paraschos:
The Shortcomings of Force-from-Motion in Robot Learning. CoRR abs/2407.02904 (2024) - [i47]Elie Aljalbout, Nikolaos Sotirakis, Patrick van der Smagt, Maximilian Karl, Nutan Chen:
LIMT: Language-Informed Multi-Task Visual World Models. CoRR abs/2407.13466 (2024) - [i46]Natabara Máté Gyöngyössy, Bernát Török, Csilla Farkas, Laura Lucaj, Attila Menyhárd, Krisztina Menyhard-Balázs, András Simonyi, Patrick van der Smagt, Zsolt Zodi, András Lorincz:
Assistive AI for Augmenting Human Decision-making. CoRR abs/2410.14353 (2024) - 2023
- [c64]Dafna Burema
A sector-based approach to AI ethics: Understanding ethical issues of AI-related incidents within their sectoral context. AIES 2023: 705-714 - [c63]Laura Lucaj
AI Regulation Is (not) All You Need. FAccT 2023: 1267-1279 - [c62]Elie Aljalbout, Maximilian Karl, Patrick van der Smagt:
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces. L4DC 2023: 1152-1166 - [c61]Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer:
Filter-Aware Model-Predictive Control. L4DC 2023: 1441-1454 - [c60]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. NeurIPS 2023 - [i45]Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt
Filter-Aware Model-Predictive Control. CoRR abs/2304.10246 (2023) - [i44]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. CoRR abs/2312.02019 (2023) - [i43]Elie Aljalbout, Felix Frank, Maximilian Karl, Patrick van der Smagt
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer. CoRR abs/2312.03673 (2023) - 2022
- [j23]Felix Frank
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation. IEEE Trans. Robotics 38(4): 2276-2294 (2022) - [c59]Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt, Daniel Cremers, Justin Bayer:
PRISM: Probabilistic Real-Time Inference in Spatial World Models. CoRL 2022: 161-174 - [c58]Baris Kayalibay, Atanas Mirchev, Patrick van der Smagt, Justin Bayer:
Tracking and Planning with Spatial World Models. L4DC 2022: 124-137 - [c57]Nutan Chen, Patrick van der Smagt, Botond Cseke:
Local Distance Preserving Auto-encoders using Continuous kNN Graphs. TAG-ML 2022: 55-66 - [i42]Baris Kayalibay, Atanas Mirchev, Patrick van der Smagt, Justin Bayer:
Tracking and Planning with Spatial World Models. CoRR abs/2201.10335 (2022) - [i41]Nutan Chen, Djalel Benbouzid, Francesco Ferroni, Mathis Nitschke, Luciano Pinna, Patrick van der Smagt:
Flat latent manifolds for music improvisation between human and machine. CoRR abs/2202.12243 (2022) - [i40]Nutan Chen, Patrick van der Smagt
Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs. CoRR abs/2206.05909 (2022) - [i39]Wolfgang E. Kerzendorf, Nutan Chen, Jack O'Brien
Probabilistic Dalek - Emulator framework with probabilistic prediction for supernova tomography. CoRR abs/2209.09453 (2022) - [i38]Elie Aljalbout, Maximilian Karl, Patrick van der Smagt
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces. CoRR abs/2211.15824 (2022) - [i37]Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt
PRISM: Probabilistic Real-Time Inference in Spatial World Models. CoRR abs/2212.02988 (2022) - 2021
- [j22]Thomas Dickmann, Nikolas J. Wilhelm
An Adaptive Mechatronic Exoskeleton for Force-Controlled Finger Rehabilitation. Frontiers Robotics AI 8: 716451 (2021) - [j21]Andrea Skolik
Layerwise learning for quantum neural networks. Quantum Mach. Intell. 3(1): 1-11 (2021) - [c56]Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. ICLR 2021 - [c55]Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF. ICLR 2021 - [c54]Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt:
Latent Matters: Learning Deep State-Space Models. NeurIPS 2021: 10234-10245 - [i36]Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt:
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. CoRR abs/2101.07046 (2021) - [i35]Felix Frank, Alexandros Paraschos, Patrick van der Smagt, Botond Cseke:
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation. CoRR abs/2101.12561 (2021) - 2020
- [j20]Patrick van der Smagt
When Machine Learning Implies Intelligence [Young Professionals]. IEEE Robotics Autom. Mag. 27(2): 19 (2020) - [j19]Nutan Chen
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images. Robotica 38(7): 1242-1262 (2020) - [c53]Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR 2020 - [c52]Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt:
Learning Flat Latent Manifolds with VAEs. ICML 2020: 1587-1596 - [i34]Nutan Chen, Alexej Klushyn, Francesco Ferroni, Justin Bayer, Patrick van der Smagt
Learning Flat Latent Manifolds with VAEs. CoRR abs/2002.04881 (2020) - [i33]Philip Becker-Ehmck, Maximilian Karl
Learning to Fly via Deep Model-Based Reinforcement Learning. CoRR abs/2003.08876 (2020) - [i32]Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF. CoRR abs/2006.10178 (2020) - [i31]Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, Martin Leib:
Layerwise learning for quantum neural networks. CoRR abs/2006.14904 (2020) - [i30]Wolfgang E. Kerzendorf
Dalek - a deep-learning emulator for TARDIS. CoRR abs/2007.01868 (2020)
2010 – 2019
- 2019
- [c51]Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Multi-Source Neural Variational Inference. AAAI 2019: 4114-4121 - [c50]Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt
On Deep Set Learning and the Choice of Aggregations. ICANN (1) 2019: 444-457 - [c49]Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, Patrick van der Smagt
Fast Approximate Geodesics for Deep Generative Models. ICANN (2) 2019: 554-566 - [c48]Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt
Increasing the Generalisaton Capacity of Conditional VAEs. ICANN (2) 2019: 779-791 - [c47]Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt
Switching Linear Dynamics for Variational Bayes Filtering. ICML 2019: 553-562 - [c46]Felix Frank
ORC - A Lightweight, Lightning-Fast Middleware. IRC 2019: 337-343 - [c45]Maximilian Karl, Philip Becker-Ehmck, Maximilian Soelch, Djalel Benbouzid, Patrick van der Smagt
Unsupervised Real-Time Control Through Variational Empowerment. ISRR 2019: 158-173 - [c44]Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. NeurIPS 2019: 2866-2875 - [c43]Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt
Approximate Bayesian Inference in Spatial Environments. Robotics: Science and Systems 2019 - [p6]Rachel Hornung, Nutan Chen, Patrick van der Smagt
Early integration for movement modeling in latent spaces. The Handbook of Multimodal-Multisensor Interfaces, Volume 3 (3) 2019 - [i29]Georgi Dikov, Patrick van der Smagt
Bayesian Learning of Neural Network Architectures. CoRR abs/1901.04436 (2019) - [i28]Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, Justin Bayer:
On Deep Set Learning and the Choice of Aggregations. CoRR abs/1903.07348 (2019) - [i27]Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. CoRR abs/1905.04982 (2019) - [i26]Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt:
Switching Linear Dynamics for Variational Bayes Filtering. CoRR abs/1905.12434 (2019) - [i25]Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt:
Increasing the Generalisaton Capacity of Conditional VAEs. CoRR abs/1908.08750 (2019) - [i24]Nutan Chen, Göran Westling, Benoni B. Edin, Patrick van der Smagt:
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images. CoRR abs/1909.05659 (2019) - [i23]Adnan Akhundov, Maximilian Soelch, Justin Bayer, Patrick van der Smagt
Variational Tracking and Prediction with Generative Disentangled State-Space Models. CoRR abs/1910.06205 (2019) - [i22]Neha Das, Maximilian Karl
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations. CoRR abs/1911.00756 (2019) - 2018
- [c42]Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt
Metrics for Deep Generative Models. AISTATS 2018: 1540-1550 - [c41]Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt
Active Learning based on Data Uncertainty and Model Sensitivity. IROS 2018: 1547-1554 - [i21]Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer:
Approximate Bayesian inference in spatial environments. CoRR abs/1805.07206 (2018) - [i20]Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt:
Active Learning based on Data Uncertainty and Model Sensitivity. CoRR abs/1808.02026 (2018) - [i19]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. CoRR abs/1811.04451 (2018) - [i18]Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, Patrick van der Smagt:
Fast Approximate Geodesics for Deep Generative Models. CoRR abs/1812.08284 (2018) - 2017
- [j18]Egidio Falotico
Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. Frontiers Neurorobotics 11: 2 (2017) - [j17]Hannes Höppner
Key Insights into Hand Biomechanics: Human Grip Stiffness Can Be Decoupled from Force by Cocontraction and Predicted from Electromyography. Frontiers Neurorobotics 11: 17 (2017) - [c40]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. ICLR (Poster) 2017 - [c39]Jörn Vogel, Naohiro Takemura, Hannes Höppner
Hitting the sweet spot: Automatic optimization of energy transfer during tool-held hits. ICRA 2017: 1549-1556 - [c38]Rui Zhao, Haider Ali, Patrick van der Smagt
Two-stream RNN/CNN for action recognition in 3D videos. IROS 2017: 4260-4267 - [i17]Baris Kayalibay, Grady Jensen, Patrick van der Smagt:
CNN-based Segmentation of Medical Imaging Data. CoRR abs/1701.03056 (2017) - [i16]Rui Zhao, Haider Ali, Patrick van der Smagt:
Two-Stream RNN/CNN for Action Recognition in 3D Videos. CoRR abs/1703.09783 (2017) - [i15]Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt:
Metrics for Deep Generative Models. CoRR abs/1711.01204 (2017) - [i14]Sebastian Urban, Patrick van der Smagt:
Automatic Differentiation for Tensor Algebras. CoRR abs/1711.01348 (2017) - [i13]Sebastian Urban, Marcus Basalla, Patrick van der Smagt
Gaussian Process Neurons Learn Stochastic Activation Functions. CoRR abs/1711.11059 (2017) - 2016
- [j16]Christoph Richter
Musculoskeletal Robots: Scalability in Neural Control. IEEE Robotics Autom. Mag. 23(4): 128-137 (2016) - [c37]Andreas Blenk
Boost online virtual network embedding: Using neural networks for admission control. CNSM 2016: 10-18 - [c36]Nutan Chen, Maximilian Karl, Patrick van der Smagt
Dynamic movement primitives in latent space of time-dependent variational autoencoders. Humanoids 2016: 629-636 - [c35]Herke van Hoof
Stable reinforcement learning with autoencoders for tactile and visual data. IROS 2016: 3928-3934 - [p5]Patrick van der Smagt
Neurorobotics: From Vision to Action. Springer Handbook of Robotics, 2nd Ed. 2016: 2069-2094 - [i12]Christoph Richter, Sören Jentzsch, Rafael Hostettler, Jesús Alberto Garrido, Eduardo Ros, Alois C. Knoll, Florian Röhrbein, Patrick van der Smagt, Jörg Conradt:
Scalability in Neural Control of Musculoskeletal Robots. CoRR abs/1601.04862 (2016) - [i11]Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt:
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series. CoRR abs/1602.07109 (2016) - [i10]Wiebke Köpp, Patrick van der Smagt, Sebastian Urban:
A Differentiable Transition Between Additive and Multiplicative Neurons. CoRR abs/1604.03736 (2016) - [i9]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. CoRR abs/1605.06432 (2016) - [i8]Maximilian Karl, Artur Lohrer, Dhananjay Shah, Frederik Diehl
ML-based tactile sensor calibration: A universal approach. CoRR abs/1606.06588 (2016) - [i7]Maximilian Karl
Unsupervised preprocessing for Tactile Data. CoRR abs/1606.07312 (2016) - 2015
- [j15]Jörn Vogel
An assistive decision-and-control architecture for force-sensitive hand-arm systems driven by human-machine interfaces. Int. J. Robotics Res. 34(6): 763-780 (2015) - [c34]Nutan Chen, Justin Bayer, Sebastian Urban, Patrick van der Smagt
Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders. Humanoids 2015: 434-440 - [c33]Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt
FlowNet: Learning Optical Flow with Convolutional Networks. ICCV 2015: 2758-2766 - [c32]Zoltán Ádám Milacski
Robust Detection of Anomalies via Sparse Methods. ICONIP (3) 2015: 419-426 - [c31]Hannes Höppner
Two-dimensional orthoglide mechanism for revealing areflexive human arm mechanical properties. IROS 2015: 1178-1185 - [c30]Nutan Chen, Sebastian Urban, Justin Bayer, Patrick van der Smagt
Measuring fingertip forces from camera images for random finger poses. IROS 2015: 1216-1221 - [i6]Sebastian Urban, Patrick van der Smagt:
A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactions. CoRR abs/1503.05724 (2015) - [i5]Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox:
FlowNet: Learning Optical Flow with Convolutional Networks. CoRR abs/1504.06852 (2015) - [i4]Justin Bayer, Maximilian Karl
Fast Adaptive Weight Noise. CoRR abs/1507.05331 (2015) - [i3]Maximilian Karl
Efficient Empowerment. CoRR abs/1509.08455 (2015) - 2014
- [c29]Christian Osendorfer, Hubert Soyer, Patrick van der Smagt
Image Super-Resolution with Fast Approximate Convolutional Sparse Coding. ICONIP (3) 2014: 250-257 - [c28]Nutan Chen, Sebastian Urban, Christian Osendorfer, Justin Bayer, Patrick van der Smagt
Estimating finger grip force from an image of the hand using Convolutional Neural Networks and Gaussian processes. ICRA 2014: 3137-3142 - [c27]Hannes Höppner
A new biarticular joint mechanism to extend stiffness ranges. ICRA 2014: 3403-3410 - [c26]Rachel Hornung, Holger Urbanek, Julian Klodmann, Christian Osendorfer, Patrick van der Smagt
Model-free robot anomaly detection. IROS 2014: 3676-3683 - [c25]Justin Bayer, Christian Osendorfer, Nutan Chen, Sebastian Urban, Patrick van der Smagt:
On Fast Dropout and its Applicability to Recurrent Networks. ICLR (Poster) 2014 - [p4]Georg Stillfried, Ulrich Hillenbrand, Marcus Settles, Patrick van der Smagt
MRI-Based Skeletal Hand Movement Model. The Human Hand as an Inspiration for Robot Hand Development 2014: 49-75 - 2013
- [j14]Claudio Castellini
Evidence of muscle synergies during human grasping. Biol. Cybern. 107(2): 233-245 (2013) - [j13]Thomas Rückstieß, Christian Osendorfer, Patrick van der Smagt
Minimizing data consumption with sequential online feature selection. Int. J. Mach. Learn. Cybern. 4(3): 235-243 (2013) - [j12]David J. Braun
Robots Driven by Compliant Actuators: Optimal Control Under Actuation Constraints. IEEE Trans. Robotics 29(5): 1085-1101 (2013) - [c24]Justin Bayer, Christian Osendorfer, Sebastian Urban, Patrick van der Smagt
Training Neural Networks with Implicit Variance. ICONIP (2) 2013: 132-139 - [c23]Christian Osendorfer, Justin Bayer, Sebastian Urban, Patrick van der Smagt
Convolutional Neural Networks Learn Compact Local Image Descriptors. ICONIP (3) 2013: 624-630 - [c22]Jörn Vogel
Continuous robot control using surface electromyography of atrophic muscles. IROS 2013: 845-850 - [c21]Sebastian Urban, Justin Bayer, Christian Osendorfer, Göran Westling, Benoni B. Edin, Patrick van der Smagt
Computing grip force and torque from finger nail images using Gaussian processes. IROS 2013: 4034-4039 - [c20]Christian Osendorfer, Justin Bayer, Patrick van der Smagt:
Unsupervised Feature Learning for low-level Local Image Descriptors. ICLR (Workshop Poster) 2013 - [i2]Christian Osendorfer, Justin Bayer, Patrick van der Smagt:
Convolutional Neural Networks learn compact local image descriptors. CoRR abs/1304.7948 (2013) - 2012
- [j11]J. Leo van Hemmen, Patrick van der Smagt
Foreword for the special issue on Multimodal and Sensorimotor Bionics. Biol. Cybern. 106(11-12): 615-616 (2012) - [j10]Agneta Gustus, Georg Stillfried, Judith Visser, Henrik Jörntell, Patrick van der Smagt
Human hand modelling: kinematics, dynamics, applications. Biol. Cybern. 106(11-12): 741-755 (2012) - [c19]Justin Bayer, Christian Osendorfer, Patrick van der Smagt
Learning Sequence Neighbourhood Metrics. ICANN (1) 2012: 531-538 - [c18]David J. Braun
Optimal torque and stiffness control in compliantly actuated robots. IROS 2012: 2801-2808 - [c17]Dominic Lakatos, Daniel Rüschen
Identification of Human Limb Stiffness in 5 DoF and Estimation via EMG. ISER 2012: 89-99 - [p3]Patrick van der Smagt
Solving the Ill-Conditioning in Neural Network Learning. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 191-203 - 2011
- [c16]Thomas Rückstieß, Christian Osendorfer, Patrick van der Smagt
Sequential Feature Selection for Classification. Australasian Conference on Artificial Intelligence 2011: 132-141 - [c15]Dominic Lakatos, Florian Petit, Patrick van der Smagt
Conditioning vs. excitation time for estimating impedance parameters of the human arm. Humanoids 2011: 636-642 - [c14]Claudio Castellini
Preliminary evidence of dynamic muscular synergies in human grasping. ICAR 2011: 28-33 - [c13]Hannes Höppner
The Grasp Perturbator: Calibrating human grasp stiffness during a graded force task. ICRA 2011: 3312-3316 - [c12]Jörn Vogel, Claudio Castellini, Patrick van der Smagt
EMG-based teleoperation and manipulation with the DLR LWR-III. IROS 2011: 672-678 - [i1]Justin Bayer, Christian Osendorfer, Patrick van der Smagt:
Learning Sequence Neighbourhood Metrics. CoRR abs/1109.2034 (2011) - 2010
- [c11]Michael Strohmayr, Hannes P. Saal
The DLR touch sensor I: A flexible tactile sensor for robotic hands based on a crossed-wire approach. IROS 2010: 897-903 - [c10]Joern Vogel
Continuous Control of the DLR Light-Weight Robot III by a Human with Tetraplegia Using the BrainGate2 Neural Interface System. ISER 2010: 125-136
2000 – 2009
- 2009
- [j9]Claudio Castellini
Surface EMG in advanced hand prosthetics. Biol. Cybern. 100(1): 35-47 (2009) - 2008
- [j8]Markus Grebenstein
Antagonism for a Highly Anthropomorphic Hand-Arm System. Adv. Robotics 22(1): 39-55 (2008) - [c9]Claudio Castellini
Surface EMG for force control of mechanical hands. ICRA 2008: 725-730 - [p2]Michael A. Arbib, Giorgio Metta, Patrick van der Smagt
Neurorobotics: From Vision to Action. Springer Handbook of Robotics 2008: 1453-1480 - 2006
- [c8]Sebastian Bitzer, Patrick van der Smagt
Learning EMG Control of a Robotic Hand: Towards Active Prostheses. ICRA 2006: 2819-2823 - 2004
- [c7]Holger Urbanek, Alin Albu-Schäffer, Patrick van der Smagt
Learning from demonstration: repetitive movements for autonomous service robotics. IROS 2004: 3495-3500 - 2002
- [j7]Patrick van der Smagt
Guest Editorial for Special Issue on Scalable Applications of Neural Networks to Robotics. Appl. Intell. 17(1): 7-10 (2002) - [j6]Jan Peters, Patrick van der Smagt
Searching a Scalable Approach to Cerebellar Based Control. Appl. Intell. 17(1): 11-33 (2002) - 2000
- [j5]Patrick van der Smagt
Benchmarking cerebellar control. Robotics Auton. Syst. 32(4): 237-251 (2000) - [c6]Patrick van der Smagt
The cerebellum as computed torque model. KES 2000: 760-763
1990 – 1999
- 1998
- [j4]Patrick van der Smagt
Cerebellar Control of Robot Arms. Connect. Sci. 10(3-4): 301-320 (1998) - [c5]Max Fischer, Patrick van der Smagt
Learning Techniques in a Dataglove Based Telemanipulation System for the DLR Hand. ICRA 1998: 1603-1608 - 1996
- [j3]Patrick van der Smagt
Analysis and control of a rubbertuator arm. Biol. Cybern. 75(5): 433-440 (1996) - [p1]Patrick van der Smagt
Solving the Ill-Conditioning in Neural Network Learning. Neural Networks: Tricks of the Trade 1996: 193-206 - 1995
- [c4]Patrick van der Smagt
Approximation with neural networks: between local and global approximation. ICNN 1995: 1060-1064 - [c3]Patrick van der Smagt
A visually guided robot and a neural network join to grasp slanted objects. SNN Symposium on Neural Networks 1995: 121-128 - 1994
- [j2]P. Patrick van der Smagt
Minimisation methods for training feedforward neural networks. Neural Networks 7(1): 1-11 (1994) - [j1]Ted Hesselroth, Kakali Sarkar, P. Patrick van der Smagt
Neural Network Control of a Pneumatic Robot Arm. IEEE Trans. Syst. Man Cybern. Syst. 24(1): 28-38 (1994) - 1992
- [c2]P. Patrick van der Smagt
A Self-learning Controller For Monocular Grasping. IROS 1992: 177-181 - 1990
- [c1]P. Patrick van der Smagt
A Comparative Study of Neural Network Algorithms Applied to Optical Character Recognition. IEA/AIE (Vol. 2) 1990: 1037-1044
Coauthor Index
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last updated on 2024-12-10 20:51 CET by the dblp team
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