🤖 AI Summary
Current vision-language-action (VLA) models struggle to focus on task-relevant functional interaction regions due to their reliance on holistic object appearance, limiting robustness in unstructured environments. This work proposes an implicit affordance injection mechanism that leverages a zero-shot affordance teacher model to extract language-conditioned affordance representations and aligns them with intermediate visual features of the VLA model. This approach internalizes task-oriented affordance awareness without requiring explicit masks or additional modules. While preserving inference efficiency, the method reshapes visual representations to heighten sensitivity to manipulation-critical regions. Experiments demonstrate that the proposed model significantly outperforms strong baselines in both simulated and real-world environments, achieving higher task success rates and improved training efficiency.
📝 Abstract
Recent advances in Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation. However, the visual representations of most VLA models are often dominated by global object appearance and struggle to focus on task-relevant functional interaction regions, which limits their robustness in unstructured environments. Existing affordance-based methods typically rely on explicit mask injection or external perception modules, requiring additional annotations while introducing cascading perception errors and inference overhead. To address these limitations, we propose AffordVLA, an affordance-enhanced VLA framework that internalizes manipulation-centric affordance perception into VLA visual representations through implicit representation alignment. Specifically, we construct a zero-shot affordance teacher to extract task-conditioned affordance visual representations from RGB observations and language instructions. AffordVLA aligns the intermediate visual representations of the VLA with the affordance visual representations extracted by the teacher, thereby implicitly injecting manipulation-centric affordance perception into VLA visual representations and improving action accuracy. Extensive simulation and real-world experiments demonstrate that AffordVLA and its affordance teacher achieve state-of-the-art performance and outperform strong baselines. Ablation analyses show that AffordVLA effectively reshapes VLA visual representations while preserving inference efficiency, leading to improved manipulation success rates and training efficiency.