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Research questionHow can closed-loop vision-language navigation learn effectively despite distribution shift and sparse micro-action rewards?An agent’s actions change the observations and states it will encounter, causing imitation policies to face distribution shift and ambiguous supervision after deviations. Direct reinforcement learning over low-level movements is also inefficient when rewards are sparse.
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Latest papersRecent research connected to this question, newest first.Revisiting Topological Graphs for Macro Action based Closed-loop Reinforcement Learning of Vision Language Navigation in Continuous EnvironmentThe source studies vision-language navigation in continuous environments using topological graphs, frontier-node macro-actions, a training-free low-level controller, and graph-based PPO, with results on the R2R-CE and RxR-CE benchmarks.research paper · Sep 3, 2026
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