SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models

Researchers introduce SignVLA, a real-time framework enabling robots to understand and execute manipulation tasks through sign language instructions. The system combines hand-landmark extraction, attention-enhanced LSTM networks, and vision-language-action models to create an accessible human-robot interaction interface for deaf and speech-impaired users.

SignVLA addresses a critical accessibility gap in human-robot interaction by enabling sign-language-guided robotic control. Traditional VLA systems rely on speech or text input, excluding deaf and hard-of-hearing users from intuitive robot operation. This framework bridges that divide through a modular architecture that translates visual sign gestures into semantic instructions compatible with existing robotic policies.

The technical approach leverages hand landmark extraction combined with attention-enhanced LSTM networks to recognize both alphabet-level and command-level signs with temporal consistency. This design choice reflects broader trends in accessibility-first AI development, where researchers increasingly recognize that inclusive interfaces generate better overall system design. The temporal stabilization module specifically addresses real-time interaction challenges, ensuring sign recognition remains stable during fluid human-robot collaboration.

Industry implications extend beyond accessibility advocacy. This work demonstrates that lightweight temporal models can serve as effective adapters between human communication modalities and embodied AI systems. For robotics developers, integrating sign-language interfaces could unlock new market segments while improving human-robot interaction for all users through more natural gesture-based control. The modular approach suggests these techniques could integrate with existing VLA policies without requiring complete system redesigns.

The research signals growing maturity in multimodal AI accessibility. As embodied AI systems become more prevalent in manufacturing, service industries, and collaborative environments, supporting diverse communication methods becomes economically relevant beyond ethical considerations. Future development should focus on scaling sign recognition across different sign languages and testing in dynamic industrial settings to validate real-world viability.

• →SignVLA enables robots to execute manipulation tasks from sign-language instructions, expanding accessibility beyond speech and text inputs.
• →The system combines hand-landmark extraction with attention-enhanced LSTM networks to achieve real-time sign recognition with temporal stability.
• →Modular design allows the sign-to-text interface to work with downstream VLA policies without requiring complete system overhauls.
• →Lightweight temporal sign recognition demonstrates viability as an accessibility layer for embodied AI and multimodal robotics systems.
• →This research addresses a market gap where deaf and speech-impaired users have limited options for intuitive robot control interfaces.