Learning Affordances at Inference-Time for Vision-Language-Action Models

Abstract-- Solving complex real-world control tasks often takes multiple tries: if we fail at first, we reflect on what went wrong, and change our strategy accordingly to avoid making the same mistake. In robotics, Vision-Language-Action models (VLAs) offer a promising path towards solving complex control tasks, but lack the ability to contextually and dynamically readjust behavior when they fail to accomplish a task. In this work, we introduce Learning from Inference-Time Execution (LITEN), which connects a VLA low-level policy to a high-level VLM that conditions on past experiences by including them in-context, allowing it to learn the affordances and capabilities of the low-level VLA. Our approach iterates between a reasoning phase that generates and executes plans for the low-level VLA, and an assessment phase that reflects on the resulting execution and draws useful conclusions to be included in future reasoning contexts. Unlike similar approaches to self-refinement in non-robotics domains, LITEN must reflect on unstructured real-world robot trajectories (e.g., raw videos), which requires structured guiderails during assessment. Our experimental results demonstrate LITEN is able to effectively learn from past experience to generate plans that use high-affordance instructions to accomplish long-horizon tasks. Robotic foundation models based on powerful pre-trained vision-language model (VLM) backbones have the potential to combine both the semantic and common-sense problem-solving abilities of LLMs and the flexible and dexterous end-to-end control capabilities of learned policies [1], [2], [3], [4], [5]. However, current robotic foundation models, most notably Vision-Language-Action models (VLAs), have primarily been studied in "single shot" settings, where they are evaluated on their ability to follow individual user commands. A practical robotic system needs to also plan through complex behaviors and, perhaps most importantly, adjust its behavior based on context and perceived capabilities. For example, if the robot needs to open a latched container, it might try to unlatch it in a particular way, and if that fails, it should modify its strategy and try a different approach. This kind of in-context adaptation has been observed as an emergent behavior in LLMs [6], [7], [8], but has proven difficult to enable in the robotics domain with current VLAs.