Fetching the paper…
Reading the bibliography…
Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive.
Toward Global Optimization, volume 2, chapter Bayesian Methods for Seeking the Extremum
J. Mockus, V. Tiesis, and A. Zilinskas · 1978
Earlier work this paper cites.
Legged robots that balance
M. H. Raibert · 1986
Earlier work this paper cites.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
C. E. Rasmussen and C. K. I. Williams · 2005
Earlier work this paper cites.
Automatic gait optimization with gaussian process regression
D. J. Lizotte, T. Wang, M. H. Bowling, and D. Schuurmans · 2007
Earlier work this paper cites.
A Muscle-reflex Model that Encodes Principles of Legged Mechanics Produces Human Walking Dynamics and Muscle Activities
H. Geyer and H. Herr · 2010
Earlier work this paper cites.
Using response surfaces and expected improvement to optimize snake robot gait parameters
M. Tesch, J. Schneider, and H. Choset · 2011
Earlier work this paper cites.
Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning
A. Wilson, A. Fern, and P. Tadepalli · 2014
Earlier work this paper cites.
Robots that can adapt like animals
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret · 2015
Cited alongside, same era.
Optimization-based full body control for the darpa robotics challenge
S. Feng, E. Whitman, X. Xinjilefu, and C. G. Atkeson · 2015
Cited alongside, same era.
A Neural Circuitry that Emphasizes Spinal Feedback Generates Diverse Behaviours of Human Locomotion
S. Song and H. Geyer · 2015
Cited alongside, same era.
Robust spring mass model running for a physical bipedal robot
W. C. Martin, A. Wu, and H. Geyer · 2015
Cited alongside, same era.
Bayesian Optimization for Learning Gaits Under Uncertainty
R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth · 2016
Cited alongside, same era.
Sample efficient optimization for learning controllers for bipedal locomotion
R. Antonova, A. Rai, and C. G. Atkeson · 2016
Manifold gaussian processes for regression
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth · 2016
Later among the works it cites.
Terrain-adaptive locomotion skills using deep reinforcement learning
X. B. Peng, G. Berseth, and M. van de Panne · 2016
Later among the works it cites.
Taking the Human Out of the Loop: A Review of Bayesian Optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
Later among the works it cites.
Walking and running with passive compliance: Lessons from engineering a live demonstration of the atrias biped
C. Hubicki, A. Abate, P. Clary, S. Rezazadeh, M. Jones, A. Peekema, J. Van Why, R. Domres, A. Wu, W. Martin, et al · 2016
Later among the works it cites.
Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid
A. Herzog, N. Rotella, S. Mason, F. Grimminger, S. Schaal, and L. Righetti · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot
S. Kuindersma, R. Deits, M. Fallon, A. Valenzuela, H. Dai, F. Permenter, T. Koolen, P. Marion, and R. Tedrake · 2016
Cited alongside, same era.
Code available from https://github.com/nthatte/Neuromuscular-Transfemoral-Prosthesis-Model
16D Simulator for Neuromuscular Models for Biped Locomotion
Cited in the paper.
A. Marco, F. Berkenkamp, P. Hennig, A. P. Schoellig, A. Krause, S. Schaal, and S. Trimpe · 2017
Closest in time.
Experimental evaluation of deadbeat running on the atrias biped
W. C. Martin, A. Wu, and H. Geyer · 2017
Closest in time.