Can Vision-Language Models be applied in robotics?

Yes, Vision-Language Models (VLMs) can be effectively applied in robotics. These models, which combine visual understanding with natural language processing, enable robots to interpret their environment and follow human instructions more flexibly. By processing both images and text, VLMs allow robots to map visual data to actionable tasks based on verbal or written commands. This integration reduces the need for rigid, preprogrammed behaviors, making robots more adaptable to dynamic environments.

One practical application is in object manipulation and navigation. For example, a robot equipped with a VLM could receive a command like, “Move the coffee mug to the kitchen counter.” The model would first analyze camera input to identify the mug and the counter, then generate a sequence of movements to complete the task. In industrial settings, robots could use VLMs to interpret complex instructions, such as sorting items based on descriptions like “pack all red boxes first.” Another use case is human-robot interaction: a service robot could answer questions like, “Where is the nearest exit?” by analyzing its surroundings and providing a verbal response paired with directional gestures. These scenarios highlight how VLMs bridge the gap between perception and language-driven decision-making.

However, integrating VLMs into robotics poses challenges. Real-time processing is a key concern, as robots often require low-latency responses for safe operation. Running large VLMs on onboard hardware may strain computational resources, necessitating optimizations like model pruning or edge computing. Additionally, VLMs may struggle with ambiguous commands or unfamiliar environments. For instance, a vague instruction like “Tidy up the room” could lead to inconsistent results without explicit definitions of “tidiness.” Developers must address these limitations by combining VLMs with traditional robotics frameworks—using VLMs for high-level planning while relying on classical control systems for precise movements. Testing in diverse scenarios and incorporating fail-safes can further improve reliability, making VLMs a promising but supplementary tool in robotics pipelines.