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Operating robots precisely and at high speeds has been a long-standing goal of robotics research.
Consideration of risk in reinforcement learning
Matthias Heger · 1994
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Q-learning for risk-sensitive control
Vivek S Borkar · 2002
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Risk-sensitive reinforcement learning applied to control under constraints
Peter Geibel and Fritz Wysotzki · 2005
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A seven-degrees-of-freedom robot-arm driven by pneumatic artificial muscles for humanoid robots
Bertrand Tondu, Serge Ippolito, Jérémie Guiochet, and Alain Daidie · 2005
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A learning algorithm for risk-sensitive cost
Arnab Basu, Tirthankar Bhattacharyya, and Vivek S Borkar · 2008
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Integration of active and passive compliance control for safe human-robot coexistence
Riccardo Schiavi, Antonio Bicchi, and Fabrizio Flacco · 2009
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A humanoid muscle robot torso with biologically inspired construction
Ivo Boblan and Andreas Schulz · 2010
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Biorob-arm: A quickly deployable and intrinsically safe, light-weight robot arm for service robotics applications
Thomas Lens, Jürgen Kunz, Oskar Von Stryk, Christian Trommer, and Andreas Karguth · 2010
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Safe exploration of state and action spaces in reinforcement learning
Javier Garcia and Fernando Fernández · 2012
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Sensor fusion for human safety in industrial workcells
Paul Rybski, Peter Anderson-Sprecher, Daniel Huber, Chris Niessl, and Reid Simmons · 2012
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Modelling of the mckibben artificial muscle: A review
Bertrand Tondu · 2012
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Intelligent cooperative control architecture: a framework for performance improvement using safe learning
Alborz Geramifard, Joshua Redding, and Jonathan P How · 2013
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Safety control of industrial robots based on a distributed distance sensor
Giovanni Buizza Avanzini, Nicola Maria Ceriani, Andrea Maria Zanchettin, Paolo Rocco, and Luca Bascetta · 2014
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Toward safe close-proximity human-robot interaction with standard industrial robots
Przemyslaw A Lasota, Gregory F Rossano, and Julie A Shah · 2014
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Depth camera based collision avoidance via active robot control
Bernard Schmidt and Lihui Wang · 2014
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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A lightweight robotic arm with pneumatic muscles for robot learning
Dieter Büchler, Heiko Ott, and Jan Peters · 2016
Cited alongside, same era.
ISO-TS 15066: Robots and Robotic Devices: Collaborative Robots
International Organization for Standardization · 2016
Cited alongside, same era.
Co-exploring actuator antagonism and bio-inspired control in a printable robot arm
Martin F Stoelen, Fabio Bonsignorio, and Angelo Cangelosi · 2016
High-speed and lightweight humanoid robot arm for a skillful badminton robot
Shotaro Mori, Kazutoshi Tanaka, Satoshi Nishikawa, Ryuma Niiyama, and Yasuo Kuniyoshi · 2018
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Optlayer-practical constrained optimization for deep reinforcement learning in the real world
Tu-Hoa Pham, Giovanni De Magistris, and Ryuki Tachibana · 2018
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Quasi-direct drive for low-cost compliant robotic manipulation
David V Gealy, Stephen McKinley, Brent Yi, Philipp Wu, Phillip R Downey, Greg Balke, Allan Zhao, Menglong Guo, Rachel Thomasson, Anthony Sinclair, et al · 2019
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Bionic design of a 7-dof human-arm-like manipulator actuated by antagonized pneumatic artificial muscles
Daoxiong Gong, Rui He, Yu Wang, and Jianjun Yu · 2019
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Speed and spin differences between the old celluloid versus new plastic table tennis balls and the effect on the kinematic responses of elite versus sub-elite players
Marcus JC Lee, Hiroki Ozaki, Wan Xiu Goh, et al · 2019
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Cited alongside, same era.
An integrated design and fabrication strategy for entirely soft, autonomous robots
Michael Wehner, Ryan L Truby, Daniel J Fitzgerald, Bobak Mosadegh, George M Whitesides, Jennifer A Lewis, and Robert J Wood · 2016
Cited alongside, same era.
Safe model-based reinforcement learning with stability guarantees
Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, and Andreas Krause · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Control of musculoskeletal systems using learned dynamics models
Dieter Büchler, Roberto Calandra, Bernhard Schölkopf, and Jan Peters · 2018
Cited alongside, same era.
A lyapunov-based approach to safe reinforcement learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2018
Cited alongside, same era.
Safe exploration in continuous action spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa · 2018
Cited alongside, same era.
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The o80 c++ templated toolbox: Designing customized python apis for synchronizing realtime processes
Vincent Berenz, Maximilien Naveau, Felix Widmaier, Manuel Wüthrich, Jean-Claude Passy, Simon Guist, and Dieter Büchler · 2021
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Action-conditional recurrent kalman networks for forward and inverse dynamics learning
Vaisakh Shaj, Philipp Becker, Dieter Büchler, Harit Pandya, Niels van Duijkeren, C James Taylor, Marc Hanheide, and Gerhard Neumann · 2021
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Reachability-based trajectory safeguard (rts): A safe and fast reinforcement learning safety layer for continuous control
Yifei Simon Shao, Chao Chen, Shreyas Kousik, and Ram Vasudevan · 2021
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Learning to play table tennis from scratch using muscular robots
Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, and Jan Peters · 2022
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A learning-based iterative control framework for controlling a robot arm with pneumatic artificial muscles
Hao Ma, Dieter Büchler, Bernhard Schölkopf, and Michael Muehlebach · 2022
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Hidden parameter recurrent state space models for changing dynamics scenarios
Vaisakh Shaj, Dieter Buchler, Rohit Sonker, Philipp Becker, and Gerhard Neumann · 2022
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The gummiarm project: A replicable and variable-stiffness robot arm for experiments on embodied ai
Martin F Stoelen, Ricardo de Azambuja, Beatriz López Rodríguez, Fabio Bonsignorio, and Angelo Cangelosi · 2022
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