LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation
arXiv:2511.02239v2 Announce Type: replace-cross Abstract: 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 more holistic grounding. An agent capable of both acting and explaining its actions can form richer interna
The increasing reliance on large-scale models for robotic manipulation highlights the current limitations of one-way language-to-action paradigms, necessitating advancements for richer contextual understanding.
This development addresses a critical gap in robotic intelligence, paving the way for more robust, generalizable, and explainable robotic systems capable of understanding and adapting to complex environments.
Robots will transition from passively executing instructions to actively interpreting and explaining their actions, enabling self-improvement and better human-robot collaboration.
- · Robotics companies
- · AI research labs
- · Automation sector
- · Companies relying on narrow, one-way robotic control systems
Robots will become more capable of understanding and explaining their actions, leading to increased trust and broader adoption in complex tasks.
The ability of robots to self-improve through linguistic feedback loops will accelerate the development of highly autonomous and adaptable robotic systems.
More sophisticated robotic cognitive architectures could enable robots to contribute to scientific discovery and complex problem-solving in unforeseen ways.
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Read at arXiv cs.AI