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On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
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Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end behavioural cloning methods. In this paper, we explore an alternative paradigm which decomposes reasoning into three phases. First, a retrieval phase, which informs the robot what it can do with an object. Second, an alignment phase, which informs the robot where to interact with the object. And third, a replay phase, which informs the robot how to interact with the object. Through a series of real-world experiments on everyday tasks, such as grasping, pouring, and inserting objects, we show that this decomposition brings unprecedented learning efficiency, and effective inter- and intra-class generalisation. Videos are available at https://www.robot-learning.uk/retrieval-alignment-replay.
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Cited by 1 Pith paper
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Bridging Symbolic Control and Neural Reasoning in LLM Agents -- The Structured Cognitive Loop
A five-module LLM agent loop (retrieval, cognition, control, action, memory) is claimed to eliminate policy violations and redundant calls, though validation does not compare against real baselines.
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