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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.11000 |
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| _version_ | 1866911255158063104 |
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| author | Cho, Minwoo Jang, Jaehwi Park, Daehyung |
| author_facet | Cho, Minwoo Jang, Jaehwi Park, Daehyung |
| contents | We aim to solve the problem of temporal-constraint learning from demonstrations to reproduce demonstration-like logic-constrained behaviors. Learning logic constraints is challenging due to the combinatorially large space of possible specifications and the ill-posed nature of non-Markovian constraints. To figure it out, we introduce a novel temporal-constraint learning method, which we call inverse logic-constraint learning (ILCL). Our method frames ICL as a two-player zero-sum game between 1) a genetic algorithm-based temporal-logic mining (GA-TL-Mining) and 2) logic-constrained reinforcement learning (Logic-CRL). GA-TL-Mining efficiently constructs syntax trees for parameterized truncated linear temporal logic (TLTL) without predefined templates. Subsequently, Logic-CRL finds a policy that maximizes task rewards under the constructed TLTL constraints via a novel constraint redistribution scheme. Our evaluations show ILCL outperforms state-of-the-art baselines in learning and transferring TL constraints on four temporally constrained tasks. We also demonstrate successful transfer to real-world peg-in-shallow-hole tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ILCL: Inverse Logic-Constraint Learning from Temporally Constrained Demonstrations Cho, Minwoo Jang, Jaehwi Park, Daehyung Robotics We aim to solve the problem of temporal-constraint learning from demonstrations to reproduce demonstration-like logic-constrained behaviors. Learning logic constraints is challenging due to the combinatorially large space of possible specifications and the ill-posed nature of non-Markovian constraints. To figure it out, we introduce a novel temporal-constraint learning method, which we call inverse logic-constraint learning (ILCL). Our method frames ICL as a two-player zero-sum game between 1) a genetic algorithm-based temporal-logic mining (GA-TL-Mining) and 2) logic-constrained reinforcement learning (Logic-CRL). GA-TL-Mining efficiently constructs syntax trees for parameterized truncated linear temporal logic (TLTL) without predefined templates. Subsequently, Logic-CRL finds a policy that maximizes task rewards under the constructed TLTL constraints via a novel constraint redistribution scheme. Our evaluations show ILCL outperforms state-of-the-art baselines in learning and transferring TL constraints on four temporally constrained tasks. We also demonstrate successful transfer to real-world peg-in-shallow-hole tasks. |
| title | ILCL: Inverse Logic-Constraint Learning from Temporally Constrained Demonstrations |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.11000 |