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Thea, a harness in which an agentic loop orchestrates robot capabilities, each wrapped as a callable tool, inherits the core components of coding agents, modified as the physical world requires.
This work examines whether a robot's action representations preserve the semantic structure captured by pretrained encoders and introduces a plug-and-play method that anchors action representations to a semantic manifold while decomposing representations into a shared semantic channel and a private channel, all discarded at inference, leaving the deployed model unchanged.
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