A zero-shot STL planner combines a map-conditioned Transformer with a disjunctive heuristic and Transitive RL to achieve better generalization across dynamic semantic maps.
Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273–1286, 2021
2 Pith papers cite this work. Polarity classification is still indexing.
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SeedPolicy introduces self-evolving gated attention to extend the temporal horizon of diffusion policies, yielding 36.8% and 169% relative gains over standard DP on clean and randomized RoboTwin 2.0 tasks.
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Zero-Shot Signal Temporal Logic Planning with Disjunctive Branch Selection in Dynamic Semantic Maps
A zero-shot STL planner combines a map-conditioned Transformer with a disjunctive heuristic and Transitive RL to achieve better generalization across dynamic semantic maps.
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SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
SeedPolicy introduces self-evolving gated attention to extend the temporal horizon of diffusion policies, yielding 36.8% and 169% relative gains over standard DP on clean and randomized RoboTwin 2.0 tasks.