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In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators.
On the theory of the brownian motion
G. E. Uhlenbeck and L. S. Ornstein · 1930
Earlier work this paper cites.
Real-time obstacle avoidance for manipulators and mobile robots
O. Khatib · 1986
Earlier work this paper cites.
A unified approach for motion and force control of robot manipulators: The operational space formulation
O. Khatib · 1987
Earlier work this paper cites.
Elastic strips: A framework for motion generation in human environments
O. Brock and O. Khatib · 2002
Earlier work this paper cites.
Design and use paradigms for gazebo, an open-source multi-robot simulator
N. Koenig and A. Howard · 2004
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.
Collision detection and reaction: A contribution to safe physical human-robot interaction
S. Haddadin, A. Albu-Schaffer, A. De Luca, and G. Hirzinger · 2008
Earlier work this paper cites.
Reactive Motion Planning for Mobile Robots
Abraham, R. Cuautle, M. Osorio, and R. Zapata · 2008
Earlier work this paper cites.
Real-time motion planning with applications to autonomous urban driving
Y. Kuwata, J. Teo, G. Fiore, S. Karaman, E. Frazzoli, and J. P. How · 2009
Earlier work this paper cites.
Real-time reactive motion generation based on variable attractor dynamics and shaped velocities
S. Haddadin, H. Urbanek, S. Parusel, D. Burschka, J. Roßmann, A. Albu-Schäffer, and G. Hirzinger · 2010
Earlier work this paper cites.
Vision based obstacle avoidance techniques
M. S. Guzel and R. Bicker · 2011
Earlier work this paper cites.
A dynamical system approach to realtime obstacle avoidance
S. M. Khansari-Zadeh and A. Billard · 2012
Earlier work this paper cites.
Probabilistic movement primitives
A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann · 2013
Earlier work this paper cites.
Reinforcement learning in robotics: A survey
J. Kober, J. A. Bagnell, and J. Peters · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Safe exploration techniques for reinforcement learning–an overview
M. Pecka and T. Svoboda · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Understanding the geometry of workspace obstacles in motion optimization
N. Ratliff, M. Toussaint, and S. Schaal · 2015
Cited alongside, same era.
Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio · 2015
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T. Schaul, J. Quan, I. Antonoglou, and D. Silver · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Dronet: Learning to fly by driving
A. Loquercio, A. I. Maqueda, C. R. Del-Blanco, and D. Scaramuzza · 2018
Later among the works it cites.
Deep reinforcement learning for collision avoidance of robotic manipulators
B. Sangiovanni, A. Rendiniello, G. P. Incremona, A. Ferrara, and M. Piastra · 2018
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T. Silver, K. Allen, J. Tenenbaum, and L. Kaelbling · 2018
Later among the works it cites.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
Later among the works it cites.
Addressing function approximation error in actor-critic methods
S. Fujimoto, H. Van Hoof, and D. Meger · 2018
Later among the works it cites.
Deep reinforcement learning that matters
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2016
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Stable reinforcement learning with autoencoders for tactile and visual data
H. Van Hoof, N. Chen, M. Karl, P. van der Smagt, and J. Peters · 2016
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Learning feedback terms for reactive planning and control
A. Rai, G. Sutanto, S. Schaal, and F. Meier · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine · 2017
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Real-time perception meets reactive motion generation
D. Kappler, F. Meier, J. Issac, J. Mainprice, C. G. Cifuentes, M. Wüthrich, V. Berenz, S. Schaal, N. Ratliff, and J. Bohg · 2018
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Deepim: Deep iterative matching for 6d pose estimation
Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox · 2018
Cited alongside, same era.
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Later among the works it cites.
Addressing function approximation error in actor-critic methods
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Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation
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R. Martín-Martín, M. A. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg · 2019
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Quantile qt-opt for risk-aware vision-based robotic grasping
C. Bodnar, A. Li, K. Hausman, P. Pastor, and M. Kalakrishnan · 2019
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Residual reinforcement learning for robot control
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Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks
M. A. Lee, Y. Zhu, K. Srinivasan, P. Shah, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg · 2019
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The ingredients of real-world robotic reinforcement learning
H. Zhu, J. Yu, A. Gupta, D. Shah, K. Hartikainen, A. Singh, V. Kumar, and S. Levine · 2020
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Robust vision-based obstacle avoidance for micro aerial vehicles in dynamic environments
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How to make deep rl work in practice
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