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Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects.
A case study of flexible object manipulation
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Generative adversarial nets
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Combined task and motion planning through an extensible planner-independent interface layer
Robust locally-linear controllable embedding
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S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel · 2014
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Continuous control with deep reinforcement learning
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Embed to control: A locally linear latent dynamics model for control from raw images
M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
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Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
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Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
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Visual reinforcement learning with imagined goals
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Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds
J. Sung, S. H. Jin, and A. Saxena · 2018
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Learning robotic assembly from cad
G. Thomas, M. Chien, A. Tamar, J. A. Ojea, and P. Abbeel · 2018
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Bias and generalization in deep generative models: An empirical study
S. Zhao, H. Ren, A. Yuan, J. Song, N. Goodman, and S. Ermon · 2018
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