Fetching the paper…
Reading the bibliography…
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position.
A comparison of direct and model-based reinforcement learning
C. G. Atkeson and J. C. Santamaria · 1997
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
R. S. Sutton, D. Precup, and S. Singh · 1999
Earlier work this paper cites.
Movement imitation with nonlinear dynamical systems in humanoid robots
A. J. Ijspeert, J. Nakanishi, and S. Schaal · 2002
Earlier work this paper cites.
Learning attractor landscapes for learning motor primitives
A. J. Ijspeert, J. Nakanishi, and S. Schaal · 2003
Earlier work this paper cites.
Reinforcement learning for humanoid robotics
J. Peters, S. Vijayakumar, and S. Schaal · 2003
Earlier work this paper cites.
Geometric Control of Mechanical Systems
F. Bullo and A. D. Lewis · 2005
Earlier work this paper cites.
Dynamic movement primitives-a framework for motor control in humans and humanoid robotics
S. Schaal · 2006
Earlier work this paper cites.
Learning motor primitives for robotics
J. Kober and J. Peters · 2009
Earlier work this paper cites.
Learning and generalization of motor skills by learning from demonstration
P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal · 2009
Earlier work this paper cites.
Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies
S. Calinon, I. Sardellitti, and D. G. Caldwell · 2010
Earlier work this paper cites.
Robot motor skill coordination with em-based reinforcement learning
P. Kormushev, S. Calinon, and D. G. Caldwell · 2010
Earlier work this paper cites.
Task-specific generalization of discrete and periodic dynamic movement primitives
A. Ude, A. Gams, T. Asfour, and J. Morimoto · 2010
Earlier work this paper cites.
Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. E. Rasmussen · 2011
Earlier work this paper cites.
Skill learning and task outcome prediction for manipulation
P. Pastor, M. Kalakrishnan, S. Chitta, E. Theodorou, and S. Schaal · 2011
Earlier work this paper cites.
Reinforcement learning with sequences of motion primitives for robust manipulation
F. Stulp, E. A. Theodorou, and S. Schaal · 2012
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
Earlier work this paper cites.
A survey on policy search for robotics
M. P. Deisenroth, G. Neumann, and J. Peters · 2013
Earlier work this paper cites.
Dynamical movement primitives: Learning attractor models for motor behaviors
A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal · 2013
Cited alongside, same era.
Learning to select and generalize striking movements in robot table tennis
K. Mülling, J. Kober, O. Kroemer, and J. Peters · 2013
Cited alongside, same era.
Dynamic movement primitives for human-robot interaction: Comparison with human behavioral observation
M. Prada, A. Remazeilles, A. Koene, and S. Endo · 2013
Cited alongside, same era.
Gaussian processes for data-efficient learning in robotics and control
M. P. Deisenroth, D. Fox, and C. E. Rasmussen · 2015
Cited alongside, same era.
Learning robot motions with stable dynamical systems under diffeomorphic transformations
K. Neumann and J. Steil · 2015
Cited alongside, same era.
Reinforcement learning vs human programming in tetherball robot games
Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Later among the works it cites.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
K. Chua, R. Calandra, R. McAllister, and S. Levine · 2018
Later among the works it cites.
Pytorch implementations of reinforcement learning algorithms
I. Kostrikov · 2018
Later among the works it cites.
Deep encoder-decoder networks for mapping raw images to dynamic movement primitives
R. Pahic, A. Gams, A. Ude, and J. Morimoto · 2018
Later among the works it cites.
Learning task-parameterized dynamic movement primitives using mixture of gmms
A. Pervez and D. Lee · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Parisi, H. Abdulsamad, A. Paraschos, C. Daniel, and J. Peters · 2015
Cited alongside, same era.
A tutorial on task-parameterized movement learning and retrieval
S. Calinon · 2016
Cited alongside, same era.
Dynamic movement primitives in latent space of time-dependent variational autoencoders
N. Chen, M. Karl, and P. Van Der Smagt · 2016
Cited alongside, same era.
Hierarchical relative entropy policy search
C. Daniel, G. Neumann, O. Kroemer, and J. Peters · 2016
Cited alongside, same era.
Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems
N. Perrin and P. Schlehuber-Caissier · 2016
Cited alongside, same era.
Optnet: Differentiable optimization as a layer in neural networks
B. Amos and J. Z. Kolter · 2017
Cited alongside, same era.
The option-critic architecture
P.-L. Bacon, J. Harb, and D. Precup · 2017
Cited alongside, same era.
N. D. Ratliff, J. Issac, D. Kappler, S. Birchfield, and D. Fox · 2018
Later among the works it cites.
Learning sensor feedback models from demonstrations via phase-modulated neural networks
G. Sutanto, Z. Su, S. Schaal, and F. Meier · 2018
Later among the works it cites.
Active learning of probabilistic movement primitives
A. Conkey and T. Hermans · 2019
Later among the works it cites.
Hamiltonian neural networks
S. Greydanus, M. Dzamba, and J. Yosinski · 2019
Later among the works it cites.
Kernelized movement primitives
Y. Huang, L. Rozo, J. Silvério, and D. G. Caldwell · 2019
Later among the works it cites.
Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks
R. Martin-Martin, M. A. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg · 2019
Later among the works it cites.
W. Whitney, R. Agarwal, K. Cho, and A. Gupta · 2019
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2019
Later among the works it cites.
Ego-pose estimation and forecasting as real-time pd control
Y. Yuan and K. Kitani · 2019
Later among the works it cites.
M. Cranmer, S. Greydanus, S. Hoyer, P. Battaglia, D. Spergel, and S. Ho · 2020
Closest in time.
Euclideanizing flows: Diffeomorphic reduction for learning stable dynamical systems
M. A. Rana, A. Li, D. Fox, B. Boots, F. Ramos, and N. Ratliff · 2020
Closest in time.
Robot modeling and control
M. W. Spong, S. Hutchinson, and M. Vidyasagar · 2020
Closest in time.