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Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics.
Difftaichi: Differentiable programming for physical simulation
Y. Hu, L. Anderson, T.-M. Li, Q. Sun, N. Carr, J. Ragan-Kelley, and F. Durand · 1910
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Concurrent design optimization of mechanical structure and control for high speed robots
J.-H. Park and H. Asada · 1994
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Evolving virtual creatures
K. Sims · 1994
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Automatic design and manufacture of robotic lifeforms
H. Lipson and J. B. Pollack · 2000
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The road less travelled: Morphology in the optimization of biped robot locomotion
C. Paul and J. C. Bongard · 2001
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Completely derandomized self-adaptation in evolution strategies
N. Hansen and A. Ostermeier · 2001
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Evolution of the human hand: the role of throwing and clubbing
R. W. Young · 2003
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Kinetostatic analysis of underactuated fingers
L. Birglen and C. M. Gosselin · 2004
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Is achilles tendon compliance optimised for maximum muscle efficiency during locomotion?
G. Lichtwark and A. Wilson · 2007
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Neural bases of hand synergies
M. Santello, G. Baud-Bovy, and H. Jörntell · 2013
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Flexible muscle-based locomotion for bipedal creatures
T. Geijtenbeek, M. Van De Panne, and A. F. Van Der Stappen · 2013
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A compliant, underactuated hand for robust manipulation
L. U. Odhner, L. P. Jentoft, M. R. Claffee, N. Corson, Y. Tenzer, R. R. Ma, M. Buehler, R. Kohout, R. D. Howe, and A. M. Dollar · 2014
Cited alongside, same era.
Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding
N. Cheney, R. MacCurdy, J. Clune, and H. Lipson · 2014
Cited alongside, same era.
Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Cited alongside, same era.
Associations between balance and muscle strength, power performance in male youth athletes of different maturity status
R. Hammami, A. Chaouachi, I. Makhlouf, U. Granacher, and D. G. Behm · 2016
Cited alongside, same era.
Real-world evolution adapts robot morphology and control to hardware limitations
T. F. Nygaard, C. P. Martin, E. Samuelsen, J. Torresen, and K. Glette · 2018
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Reinforcement learning for improving agent design
D. Ha · 2018
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Kinematic synthesis using reinforcement learning
K. Vermeer, R. Kuppens, and J. Herder · 2018
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End-to-end differentiable physics for learning and control
F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter · 2018
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Data-efficient learning of morphology and controller for a microrobot
T. Liao, G. Wang, B. Yang, R. Lee, K. Pister, S. Levine, and R. Calandra · 2019
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Jointly learning to construct and control agents using deep reinforcement learning
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Topological evolution for embodied cellular automata
N. Cheney and H. Lipson · 2016
Cited alongside, same era.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine · 2017
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Cited alongside, same era.
Learning dexterous in-hand manipulation
M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al · 2018
Cited alongside, same era.
Learning to walk via deep reinforcement learning
T. Haarnoja, S. Ha, A. Zhou, J. Tan, G. Tucker, and S. Levine · 2018
Cited alongside, same era.
Computational co-optimization of design parameters and motion trajectories for robotic systems
S. Ha, S. Coros, A. Alspach, J. Kim, and K. Yamane · 2018
Cited alongside, same era.
C. Schaff, D. Yunis, A. Chakrabarti, and M. R. Walter · 2019
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Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning
K. S. Luck, H. Ben Amor, and R. Calandra · 2019
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A differentiable physics engine for deep learning in robotics
J. Degrave, M. Hermans, J. Dambre, and F. Wyffels · 2019
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Chainqueen: A real-time differentiable physical simulator for soft robotics
Y. Hu, J. Liu, A. Spielberg, J. B. Tenenbaum, W. T. Freeman, J. Wu, D. Rus, and W. Matusik · 2019
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Augmented random search for quadcopter control: An alternative to reinforcement learning
A. Kumar Tiwari and S. V. Nadimpalli · 2019
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Garage: A toolkit for reproducible reinforcement learning research
T. garage contributors · 2019
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Underactuation design for tendon-driven hands via optimization of mechanically realizable manifolds in posture and torque spaces
T. Chen, L. Wang, M. Haas-Heger, and M. Ciocarlie · 2020
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