GesVLA addresses a key limitation in existing Vision-Language-Action (VLA) models: their reliance on textual instructions alone, which often fails to resolve spatial ambiguity in cluttered scenes with multiple similar objects. The authors introduce a Gesture-Aware Embedding (GAE) module that fuses 2D hand pose estimates with language tokens before feeding them into the action generation head. The system architecture processes each input frame through three parallel streams: a vision encoder for scene understanding, a text tokenizer for language instructions, and a lightweight hand-pose detector for gesture cues. The GAE module aligns these modalities into a shared embedding space, allowing the model to resolve ambiguous references such as "pick the cup" by using the pointing direction of the human's hand. This multi-modal grounding produces significantly more reliable target selection in cluttered tabletop environments. GesVLA is evaluated both in high-fidelity simulation and on a physical 7-DoF Franka robotic arm. Results demonstrate improved success rates on ambiguous object-selection tasks compared to text-only VLAs, and the model generalizes to unseen object categories and novel gesture patterns. Additionally, the framework supports gesture-only commands—useful in noise-sensitive settings where verbal instructions are unreliable—making it a versatile interface for human-robot collaboration.