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Look Ahead: Search-Driven Reasoning in Embodied AI
Abstract:
Search lets an embodied policy look ahead before committing to an action. We study this idea in PushT by augmenting a pretrained Action Chunk Transformer with Beam Search over perturbed action chunks.
Candidate futures are evaluated in cloned simulator states and scored with coverage-based reward. This turns single-pass ACT inference into receding-horizon action selection while keeping the learned policy as the proposal mechanism.
We also introduce One Step Ahead, a lightweight gate that first simulates the nominal policy chunk and invokes full search only when the predicted coverage change suggests that additional computation is necessary.
Project Webpage:
https://azeer.co/research/lookahead/
Project Code:
https://github.com/AhmedZeer/lerobot-tree/tree/main/examples/tree_search/pusht
Видео Look Ahead: Search-Driven Reasoning in Embodied AI канала Ahmed ZEER
Search lets an embodied policy look ahead before committing to an action. We study this idea in PushT by augmenting a pretrained Action Chunk Transformer with Beam Search over perturbed action chunks.
Candidate futures are evaluated in cloned simulator states and scored with coverage-based reward. This turns single-pass ACT inference into receding-horizon action selection while keeping the learned policy as the proposal mechanism.
We also introduce One Step Ahead, a lightweight gate that first simulates the nominal policy chunk and invokes full search only when the predicted coverage change suggests that additional computation is necessary.
Project Webpage:
https://azeer.co/research/lookahead/
Project Code:
https://github.com/AhmedZeer/lerobot-tree/tree/main/examples/tree_search/pusht
Видео Look Ahead: Search-Driven Reasoning in Embodied AI канала Ahmed ZEER
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Информация о видео
17 мая 2026 г. 11:56:52
00:00:58
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Look Ahead: Search-Driven Reasoning in Embodied AI
