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1. Skill Discovery for Exploration and Planning using Deep Skill Graphs

   Bagaria, A; Senthil, J; Konidaris, G (July 2021, Proceedings of the Thirty-Eighth International Conference on Machine Learning)

   null (Ed.)

   We introduce a new skill-discovery algorithm that builds a discrete graph representation of large continuous MDPs, where nodes correspond to skill subgoals and the edges to skill policies. The agent constructs this graph during an unsupervised training phase where it interleaves discovering skills and planning using them to gain coverage over ever-increasing portions of the state-space. Given a novel goal at test time, the agent plans with the acquired skill graph to reach a nearby state, then switches to learning to reach the goal. We show that the resulting algorithm, Deep Skill Graphs, outperforms both flat and existing hierarchical reinforcement learning methods on four difficult continuous control tasks. 

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2. Skill Discovery for Exploration and Planning using Deep Skill Graphs

   Bagaria, A; Senthil, J.; Konidaris, G.D. (July 2021, Proceedings of the Thirty-Eighth International Conference on Machine Learning)

   We introduce a new skill-discovery algorithm that builds a discrete graph representation of large continuous MDPs, where nodes correspond to skill subgoals and the edges to skill policies. The agent constructs this graph during an unsupervised training phase where it interleaves discovering skills and planning using them to gain coverage over ever-increasing portions of the state-space. Given a novel goal at test time, the agent plans with the acquired skill graph to reach a nearby state, then switches to learning to reach the goal. We show that the resulting algorithm, Deep Skill Graphs, outperforms both flat and existing hierarchical reinforcement learning methods on four difficult continuous control tasks. 

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3. Skill Generalization with Verbs

   Ma, Rachel; Lam, Lyndon; Spiegel, Benjamin; Ganeshan, Aditya; Patel, Roma; Abbatematteo, Ben; Paulius, David Paulius; Tellex, Stefanie; Konidaris, George (October 2023, Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems)

   It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a given object and that are applicable to many objects. We propose a method for generalizing manipulation skills to novel objects using verbs. Our method learns a probabilistic classifier that determines whether a given object trajectory can be described by a specific verb. We show that this classifier accurately generalizes to novel object categories with an average accuracy of 76.69% across 13 object categories and 14 verbs. We then perform policy search over the object kinematics to find an object trajectory that maximizes classifier prediction for a given verb. Our method allows a robot to generate a trajectory for a novel object based on a verb, which can then be used as input to a motion planner. We show that our model can generate trajectories that are usable for executing five verb commands applied to novel instances of two different object categories on a real robot. 

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4. Measuring Forecasting Skill from Text

   Zong, Shi; Ritter, Alan; Hovy, Eduard (January 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)
5. Design Sketching: A Newfound Skill

   Kelley, T. R.; Chandler, T.; Lu, T. (October 2022, Technology and engineering teacher)

   Translation of Design Sketching Practices, Learned Via an Innovation-focused Undergraduate Program, to K-12 Schools. 

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