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Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning.
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Unshackling evolution: Evolving soft robots with multiple materials and a powerful generative encoding
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Programmable self-assembly in a thousand-robot swarm
Michael Rubenstein, Alejandro Cornejo, and Radhika Nagpal · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Continuous adaptation via meta-learning in nonstationary and competitive environments
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Semi-supervised classification with graph convolutional networks
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Mergeable nervous systems for robots
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Proximal Policy Optimization Algorithms
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Pytorch: An imperative style, high-performance deep learning library
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Learning to control self-assembling morphologies: A study of generalization via modularity
Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, and Alexei A Efros · 2019
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Jointly learning to construct and control agents using deep reinforcement learning
Charles Schaff, David Yunis, Ayan Chakrabarti, and Matthew R Walter · 2019
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Neural graph evolution: Automatic robot design
Tingwu Wang, Yuhao Zhou, Sanja Fidler, and Jimmy Ba · 2019
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Policy transfer via kinematic domain randomization and adaptation
Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, and C Karen Liu · 2020
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Attention is all you need
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Synergies in coordination: A comprehensive overview of neural, computational, and behavioral approaches
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Hardware conditioned policies for multi-robot transfer learning
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Scalable co-optimization of morphology and control in embodied machines
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Learning quadrupedal locomotion over challenging terrain
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Bayesian meta-learning for few-shot policy adaptation across robotic platforms
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Task-agnostic morphology evolution
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Rma: Rapid motor adaptation for legged robots
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My body is a cage: the role of morphology in graph-based incompatible control
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What matters in learning from offline human demonstrations for robot manipulation
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