Introduction

Generative Artificial Intelligence (GenAI) is an AI technology that produces content in various forms, such as text, images and audio. In the last few years, GenAI has quickly become a mainstream tool that the average internet user is familiar with, often labelled as the next paradigm shift. The average user of GenAI will utilise this technology through tools such as ChatGPT or DALL-E. However, the utilisation of GenAI will change the world in different ways too, one such example is using Robots integrated with GenAI.

Where will it help?

Traditional Robotics involves programming machines with pre-defined rules and instructions. Robotics then became more intelligent and adaptable with the advent of CNNs and Sensor Fusion, allowing robots to perform more complex tasks and take some intelligent approaches to unseen problems. With GenAI, Robotics has the potential to be further scaled to real-world deployments with continuous learning, and personalized reactions with users. This is made possible by Genetic Algorithms and Machine Learning techniques that enable robots to learn, adapt and improve their performance over time. Unlike traditional Robotics, where robots are designed for specific tasks, GenAI allows robots to learn and adapt to new situations. They can analyse their environment, identify patterns, and adjust their behaviour accordingly. This capability enables robots to handle dynamic and unpredictable scenarios, making them more versatile and efficient in various applications.

Another significant aspect of GenAI in Robotics is its potential for collaborative intelligence. By connecting robots to a shared AI network, they can learn from each other’s experiences, making the entire system collectively brighter. This collaborative approach enables robots to pool their knowledge, share insights, and collectively solve complex problems. As a result, the overall system becomes more robust, efficient, and capable of tackling challenges that may be beyond the capabilities of individual robots.

Indeed, armed robots providing support to human workers at factory lines is an example where Generative AI could be applied. If one robot learns of a new product or quality defect, this information can be propagated across the Robotics network, eliminating the need for individual retraining and informing of robots with each event occurrence.

A specific case

A world-leading edge computing solutions provider prototyped a system where they integrated an LLM (Large Language Model) into a robot that acted as a waiter. Customers could ask the robot for multiple different products, including products the robot had not seen before.

This type of GenAI Integrated Robotics Solution:

1. Enables a more natural language interaction. With the LLM, the communication between the human and the robot becomes more efficient and natural.
2. Removes the need for specific tasks to be coded. With LLMs, the robot can be empowered with the ability to accomplish multiple tasks simultaneously and acquire an understanding of the connections between these tasks.
3. Delivers a better comprehension of human objectives. Combining the techniques of LLMs and Reinforcement Learning from Human Feedback (RLHF), we can deploy personalization in Robotics solutions.

We can apply this Robotics solution to other problems such as surgical procedures. The primary challenge will always be unexpected complications and situations where the robot lacks training. A surgery robot utilizing GenAI could potentially exhibit the capability to handle various surgical scenarios beyond its initial training.

Based on this kind of extrapolation it’s easy to start seeing immediate solutions in other areas such as the Autonomous Mobility domain. The domain can benefit from Generative AI by being able to learn better with multimodal data and also help in generating synthetic simulated scenarios that can improve the testing and deployment of Autonomous Robots.

Limitations

For GenAI to be effective, it must possess three crucial attributes: Speed (handle tasks fast), Quality (handle tasks well) and Diversity (handle many tasks). Creating a GenAI model that can have all three is quite a challenge. Most advancements are being made by large corporations that have the resources to push the field and invent models that satisfy these three requirements. This is potentially leading to a skew in where GenAI is being used and underutilisation in less-profitable but beneficial areas such as medicine.

With Edge Computing use-cases like Robotics with ultra-fast latency such as Surgical Robots and Autonomous Delivery Ground robots, the decision time forces all intelligence to be run on the Edge. This becomes a problem when integrating the use-cases with GenAI models as LLMs and other GenAI techniques are very huge in nature and pose lots of challenges in Edge deployments. The technology foundation of LLM models and transformers by themselves introduces challenges in low-power embedded development as most optimizations for the AI accelerators and edge platform software focused on CNN’s for the past few years.

MulticoreWare’s strengths in GenAI and Robotics

The limitations of GenAI make it an uneven playing field for inventors and innovators, but solutions to this problem do exist. At MulticoreWare:

1. We optimize various transformer models for low-power, Automotive-grade Edge platforms and achieve near real-time performance with multiple sensor inputs.
2. We have the expertise to optimize the inference stack of LLMs and other transformer-based models on various Cloud & Edge platform architectures.
3. We have the ability to accelerate your Robotics Perception solutions by developing custom Transformer based Sensor Fusion models for your own use-case.

Conclusion

The impacts, risks, standardization of GenAI technologies are at nascent stages and could evolve in various directions. As the technology matures, immense applications in Robotics will advance the field and be leveraged.

Reach out to us at info@multicorewareinc.com to explore a possible collaboration with MulticoreWare on GenerativeAI & Robotics.