Biblio

Found 206 results
Author Title [ Type(Asc)] Year
Filters: Author is Steil, Jochen J.  [Clear All Filters]
Workshop Paper
Gopinathan S, Mohammadi P, Steil JJ.  2018.  Improved Human-Robot Interaction: A manipulability based approach. ICRA 2018 Workshop on Ergonomic Physical Human-Robot Collaboration.
Web Article
Rayyes R, Kubus D, Hartmann C, Steil JJ.  2017.  Learning Inverse Statics Models Efficiently. arXiv.
Thesis
Steil JJ.  1999.  Input-Output Stability of Recurrent Neural Networks.
Miscellaneous
Gienger M, Muehlig M, Steil JJ.  2011.  Robot with automatic selection of task-specific representations for imitation learning. European Patent Office.
Rolf M, Gienger M, Steil JJ.  2013.  Robot control with bootstrapping inverse kinematics.
Malekzadeh M, Queißer J, Steil JJ.  2016.  Learning the end-effector pose from demonstration for the Bionic Handling Assistant robot.
Malekzadeh M, Queißer J, Steil JJ.  2015.  Learning from demonstration for Bionic Handling Assistant robot.
Steil JJ, Wrede S.  2012.  Intelligent Interfaces between Humans and Technology. 22:2.
Chiovetto E, Mukovskiy A, Reinhart F, Khansari-Zadeh SMohammad, Billard A, Steil JJ, Giese M.  2014.  Assessment of human-likeness and naturalness of interceptive arm reaching movement accomplished by a humanoid robot. ECVP 2014 Abstracts. 43:107–107.
Wersing H, Götting M, Steil JJ.  2009.  Adaptive scene dependent filters in online learning environments.
Magazine Article
Steil JJ, Wrede S.  2019.  Maschinelles Lernen und lernende Assistenzsysteme - Neue Tätigkeiten, Rollen und Anforderungen für Beschäftigte? Berufsbildung in Wissenschaft und Praxis – BWP. 3:14-18.
Journal Article
Wischnewski M, Belardinelli A, Schneider WX, Steil JJ.  2010.  Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention Cognitive Computation. 2:326–343.
Gopinathan S, Ötting S, Steil JJ.  2017.  A user study on personalized stiffness control and task specificity in physical Human-Robot Interaction. Frontiers in Robotics and AI -- Humanoid Robotics.
Wrede S, Emmerich C, Grünberg R, Nordmann A, Swadzba A, Steil JJ.  2013.  A User Study on Kinesthetic Teaching of Redundant Robots in Task and Configuration Space. Journal of Human-Robot Interaction. 2:56–81.
Steil JJ, Cawley GC, Villmann T.  2005.  Trends in Neurocomputing at ESANN 2004. Neurocomputing. 64:1–4.
Aswolinskiy W, Reinhart F, Steil JJ.  2017.  Time Series Classification in Reservoir- and Model-Space. Neural Processing Letters. 48(3):789–809.
Junger I.B, Steil JJ.  2003.  Static Sliding Mode Phenomena in Dynamical Systems. IEEE Trans. Automatic Control. 48:680–686.
Soltoggio A, Steil JJ.  2013.  Solving the distal reward problem with rare correlations. Neural Computation. 25:940–978.
Steil JJ, Röthling F, Haschke R, Ritter H.  2004.  Situated robot learning for multi-modal instruction and imitation of grasping. Robotics and Autonomous Systems. 47:129–141.
Vollmer A-L, Mühlig M, Steil JJ, Pitsch K, Fritsch J, Rohlfing K, Wrede B.  2014.  Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning. PLoS ONE. 9:e91349.
Steil JJ, Finas D, Beck S, Manzeschke A, Haux R.  2019.  Robotic Systems in Operating Theatres: New Forms of Team-Machine Interaction in Health Care - On Challenges for Health Information Systems on Adequately Considering Hybrid Action of Humans and Machines. Methods of Information in Medicine. 58:e14-e25.
Neumann K, Rolf M, Steil JJ.  2013.  Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 21:35–50.
Neumann K, Emmerich C, Steil JJ.  2012.  Regularization by Intrinsic Plasticity and its Synergies with Recurrence for Random Projection Methods. Journal of Intelligent Learning Systems and Applications. 4:230–246.
Reinhart F, Steil JJ.  2012.  Regularization and stability in reservoir networks with output feedback. Neurocomputing. 90:96–105.
Soltoggio A, Lemme A, Reinhart F, Steil JJ.  2013.  Rare neural correlations implement robotic conditioning with reward delays and disturbances. Frontiers in Neurorobotics. 7:6.

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