Learning Parameterized Skills from Demonstrations
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915582357536768 |
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| author | Gupta, Vedant Fu, Haotian Luo, Calvin Jiang, Yiding Konidaris, George |
| author_facet | Gupta, Vedant Fu, Haotian Luo, Calvin Jiang, Yiding Konidaris, George |
| contents | We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy that selects the appropriate discrete skill and continuous parameters at each timestep. Using a combination of temporal variational inference and information-theoretic regularization methods, we address the challenge of degeneracy common in latent variable models, ensuring that the learned skills are temporally extended, semantically meaningful, and adaptable. We empirically show that learning parameterized skills from multitask expert demonstrations significantly improves generalization to unseen tasks. Our method outperforms multitask as well as skill learning baselines on both LIBERO and MetaWorld benchmarks. We also demonstrate that DEPS discovers interpretable parameterized skills, such as an object grasping skill whose continuous arguments define the grasp location. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Parameterized Skills from Demonstrations Gupta, Vedant Fu, Haotian Luo, Calvin Jiang, Yiding Konidaris, George Machine Learning Artificial Intelligence Robotics We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy that selects the appropriate discrete skill and continuous parameters at each timestep. Using a combination of temporal variational inference and information-theoretic regularization methods, we address the challenge of degeneracy common in latent variable models, ensuring that the learned skills are temporally extended, semantically meaningful, and adaptable. We empirically show that learning parameterized skills from multitask expert demonstrations significantly improves generalization to unseen tasks. Our method outperforms multitask as well as skill learning baselines on both LIBERO and MetaWorld benchmarks. We also demonstrate that DEPS discovers interpretable parameterized skills, such as an object grasping skill whose continuous arguments define the grasp location. |
| title | Learning Parameterized Skills from Demonstrations |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2510.24095 |