Best Practice

Spotlight on AI-embodied agents

Explore Reply's pioneering AI-embodied agents simplifying robot control, showcased through the Spot case.

The AI revolution in robotics

In recent years, the field of robotics and artificial intelligence has witnessed remarkable advancements, particularly in the realm of Embodied AI. These advancements have been made possible through a convergence of technologies such as soft robotics, haptic feedback, and the revolutionary use of transformer-based algorithms. One key development has been the integration of AI into robotic systems, enabling them to understand and interact with the physical world more efficiently.

Thanks to pioneering algorithms like DINO (DIstillation of knowledge with NO labels), CLIP, and VC1 (Visual Cortex), which are built upon the Vision Transformer architecture, as Reply we have witnessed a significant leap in the capabilities of AI-embodied agents. These algorithms emulate the human visual attention mechanism, surpassing the performance of traditional Computer Vision models like Convolutional Neural Networks (CNNs).

The Spot Case

Reply's showcase of advanced AI-embodied robotics

At Reply, we harness visual representations to enable the Spot robot to understand the environment and perform complex tasks like navigation and object manipulation with minimal training, enhancing human-robot interaction. This enables the control of the AI agents using natural language and voice commands, eliminating the need for complex model management.

Spot's interaction begins with converting human commands spoken in natural language and voice into text through the Speech-to-Text phase, a crucial step for enabling seamless communication. The natural language text is then subjected to Task Processing, where subtasks are extracted, enabling Spot to gain a more comprehensive understanding of the user's intent. Spot's capabilities extend to Navigation Tasks, facilitated by the use of Vision Language Maps (VLMaps) from Google. These maps provide Spot with a semantic understanding of its environment, assisting in tasks such as autonomous exploration and mapping. In Manipulation Tasks, Spot employs two distinct AI models: Grounding DINO for object detection and Visual Cortex 1 for effective manipulation. DINO plays a pivotal role in accurately detecting and locating objects within Spot's surroundings, instead, Visual Cortex 1 enhances Spot's ability to interact with objects, ensuring precision and effectiveness, particularly in tasks like pick-and-place operations.

explore the future of AI-embodied agents

Interested in integrating cutting-edge AI into your robotics projects?