Prospection: Interpretable plans from language by predicting the future
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Dieter Fox | Yonatan Bisk | Jesse Thomason | Chris Paxton | Arunkumar Byravan | D. Fox | Arunkumar Byravan | Yonatan Bisk | Jesse Thomason | Chris Paxton
[1] Lior Wolf,et al. Using the Output Embedding to Improve Language Models , 2016, EACL.
[2] James A. Hendler,et al. HTN Planning: Complexity and Expressivity , 1994, AAAI.
[3] Gregory D. Hager,et al. CoSTAR: Instructing collaborative robots with behavior trees and vision , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[4] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[5] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[6] Allan Jabri,et al. Universal Planning Networks , 2018, ICML.
[7] Sergey Levine,et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..
[8] Timothy D. Wilson,et al. Prospection: Experiencing the Future , 2007, Science.
[9] Sergey Levine,et al. Deep visual foresight for planning robot motion , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[10] Sergey Levine,et al. Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.
[11] Peter Stone,et al. Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" , 2016, IJCAI.
[12] Peter Stone,et al. Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions , 2018, AAAI.
[13] Peter Corke,et al. Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach , 2018, Robotics: Science and Systems.
[14] Gregory D. Hager,et al. Evaluating Methods for End-User Creation of Robot Task Plans , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[15] Craig A. Knoblock,et al. PDDL-the planning domain definition language , 1998 .
[16] Razvan Pascanu,et al. Imagination-Augmented Agents for Deep Reinforcement Learning , 2017, NIPS.
[17] Daniel Marcu,et al. Natural Language Communication with Robots , 2016, NAACL.
[18] Omer Levy,et al. Simulating Action Dynamics with Neural Process Networks , 2017, ICLR.
[19] Stephen Tyree,et al. Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[20] Silvio Savarese,et al. Neural Task Programming: Learning to Generalize Across Hierarchical Tasks , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[21] Xinyu Liu,et al. Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.
[22] Stefanie Tellex,et al. Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities , 2017, Robotics: Science and Systems.
[23] Razvan Pascanu,et al. Learning model-based planning from scratch , 2017, ArXiv.
[24] Nicholas Roy,et al. Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators , 2016, Robotics: Science and Systems.
[25] Gregory D. Hager,et al. Visual Robot Task Planning , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[26] Dieter Fox,et al. SE3-nets: Learning rigid body motion using deep neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Murray Shanahan,et al. Towards Deep Symbolic Reinforcement Learning , 2016, ArXiv.
[29] Daniel Marcu,et al. Learning Interpretable Spatial Operations in a Rich 3D Blocks World , 2017, AAAI.
[30] Ross A. Knepper,et al. DeepMPC: Learning Deep Latent Features for Model Predictive Control , 2015, Robotics: Science and Systems.