The
Future Of Generative AI in Robotics Delivery

In this episode of The Future Of, Daniel Rosenstein, Group Product Manager of Advanced Autonomy and Applied Robotics at Microsoft, joins host Jeff Dance to discuss the future of generative AI in robotics. They share the importance of generative AI in robotics, particularly the integration of visual libraries and video analysis, how generative AI enhances human-robot collaboration, and the role robotics will play in our everyday lives in the near future.

Bridging Robotics, Code, and Language

Exemplified by models such as GPT-4, generative AI has emerged as a game-changer in robotics. At its core, it serves as a universal translator that understands and predicts the semantics and structure of various languages, computer codes, and data structures.

Generative AI can generate content and extract the essence from its structure, a skill that can be seen in models like GPT-4. This helps translate from one code to another, from human language to code, and even data structures to language.

The Power of Generative AI and Visual Integration

When integrated with visual libraries and video capabilities, generative AI has significant implications for robotics. Humans mainly perceive the world through vision. By embedding vision into AI, we bridge the gap between human-like perception and machine understanding.

A robot should be able to sense, think, and act in a physical environment accurately. So, it must understand its surroundings, and this is where the importance of vision and video analysis comes in.

Moreover, generative AI can seamlessly integrate varied data streams into Large Language Models (LLMs), enabling a broader context awareness. For example, an LLM could identify a country based on specific criteria and generate an image of its most iconic landmark.

Revolutionizing Robot Interactions and Efficiency

Considering the aging baby boomer population, robots can understand and act on higher-level commands. Instead of simple commands, they can now interpret things like “find my pills” or “heat my food.”

When it comes to consumer experience, generative AI improves robots’ ability to interact contextually and semantically with their surroundings. As a result, voice assistants like Siri or Alexa, integrated into robots, can complete more complex tasks.

In organizations like oil rigs or power plants, generative AI boosts robots to conduct regular inspections by providing contextual relevance to dynamic environments. Robots can interpret various sensory data, which results in more effective inspections.

Championing Data Privacy and Interconnectivity in Robotics

Major companies like Google, Meta, and Amazon are investing heavily in integrating visual perception capabilities into robotic reasoning and context. Microsoft’s offering through Azure OpenAI provides users with their unique model of OpenAI functionality along with connectors expected from Azure. Their USP centers on trust, focusing that user data remains their own when used within Azure OpenAI. Microsoft is also developing co-pilots for all major products, which will be able to interact with each other for improved functionality, marking a significant evolution in the interconnectedness of Microsoft tools.

How Generative AI Enhances the Human-Robot Alliance

Generative AI will be a powerful catalyst for robotics advancement. It might not be the definitive solution, but it holds an enormous promise for the future. One of the main challenges is when robots operate in unexpected circumstances, such as wildland fires. That’s where generative AI could come in handy and allow robots to effortlessly adapt to changing surroundings and make real-time safety decisions.

In the future, we are more likely to witness more collaboration between robots and humans. While robots work well for repetitive or dangerous jobs, they still fall short in tasks that require a more subtle human touch. As generative AI is gradually bridging this gap, it is facilitating a more integrated working relationship between humans and robots.