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A popular paradigm in robotic learning is to train a policy from scratch for every new robot.
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Ontogeny tends to recapitulate phylogeny in digital organisms
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Fast, strong and compliant pneumatic actuation for dexterous tendon-driven hands
Kumar, V., Xu, Z., and Todorov, E · 2013
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M-blocks: Momentum-driven, magnetic modular robots
Romanishin, J. W., Gilpin, K., and Rus, D · 2013
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Pybullet, a python module for physics simulation for games, robotics and machine learning, 2016
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Cad2rl: Real single-image flight without a single real image
Sadeghi, F. and Levine, S · 2016
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
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Joint optimization of robot design and motion parameters using the implicit function theorem
Ha, S., Coros, S., Alspach, A., Kim, J., and Yamane, K · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2018
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Jointly learning to construct and control agents using deep reinforcement learning
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Nervenet: Learning structured policy with graph neural networks
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Learning to control self-assembling morphologies: a study of generalization via modularity
Pathak, D., Lu, C., Darrell, T., Isola, P., and Efros, A. A · 2019
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Rajeswaran, A., Lowrey, K., Todorov, E., and Kakade, S · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
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Hardware conditioned policies for multi-robot transfer learning
Chen, T., Murali, A., and Gupta, A · 2018
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An integrated system for perception-driven autonomy with modular robots
Daudelin, J., Jing, G., Tosun, T., Yim, M., Kress-Gazit, H., and Campbell, M · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., Hoof, H., and Meger, D · 2018
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Reinforcement learning for improving agent design
Ha, D · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Hierarchically decoupled imitation for morphological transfer
Hejna, D., Pinto, L., and Abbeel, P · 2020
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One policy to control them all: Shared modular policies for agent-agnostic control
Huang, W., Mordatch, I., and Pathak, D · 2020
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My body is a cage: the role of morphology in graph-based incompatible control
Kurin, V., Igl, M., Rocktäschel, T., Boehmer, W., and Whiteson, S · 2020
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State-only imitation learning for dexterous manipulation
Radosavovic, I., Wang, X., Pinto, L., and Malik, J · 2020
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Modular robot design synthesis with deep reinforcement learning
Whitman, J., Bhirangi, R., Travers, M., and Choset, H · 2020
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Robogrammar: graph grammar for terrain-optimized robot design
Zhao, A., Xu, J., Konaković-Luković, M., Hughes, J., Spielberg, A., Rus, D., and Matusik, W · 2020
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Embodied intelligence via learning and evolution
Gupta, A., Savarese, S., Ganguli, S., and Fei-Fei, L · 2021
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Task-agnostic morphology evolution
Hejna III, D. J., Abbeel, P., and Pinto, L · 2021
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Emergent hand morphology and control from optimizing robust grasps of diverse objects
Pan, X., Garg, A., Anandkumar, A., and Zhu, Y · 2021
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Learning modular robot control policies
Whitman, J., Travers, M., and Choset, H · 2021
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