AI RESEARCH
LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation
arXiv CS.AI
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ArXi:2511.02239v2 Announce Type: replace-cross Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (L2A). However, this one-way paradigm often produces policies that execute tasks without deeper contextual understanding, limiting their ability to generalize or explain their behavior. We argue that the complementary skill of mapping actions back to language (A2L) is essential for developing holistic grounding.