Navigating the High Cost of AI Compute: What We Need for Robot Models

Navigating the High Cost of AI Compute: What We Need for Robot Models

In recent years, there have been significant advancements in the field of artificial intelligence (AI). However, one of the major challenges faced by researchers and developers is the high cost of AI compute. This article aims to explore this issue and discuss potential solutions.

One of the key areas where AI compute is required is in the development of large-scale language models (LLMs) for robots. These models enable robots to understand and generate human-like language, facilitating better communication and interaction with humans. However, existing LLMs lack real-world knowledge, making it difficult for them to apply their reasoning outputs in practical robot scenarios.

Another challenge arises when it comes to robotic manipulation tasks. While LLMs and value-based language models (VLMs) have been useful for semantic reasoning and textual prompts, they struggle to provide actionable instructions for specific robot movements such as arm positioning or chassis movement. This mismatch between the capabilities of large models and the requirements of robots hinders their performance in various scenarios, including object grasping, learning new actions, and tasks that require dexterity and precise reasoning.

Furthermore, the current state of embodied multimodal language models (EMLMs) and value maps for robotic manipulation is still in its early stages. While these models show potential in basic manipulation tasks, they fall short in more complex operations such as assembling furniture or delicately handling objects. For instance, experiments with RT-2, a robot model, demonstrated instances where it failed to consider the rolling motion of a cylindrical pen or the balance of a banana while pushing it.

It is crucial to understand that simply providing text and images as inputs to robots is insufficient for them to acquire specialized skills. These skills range from simple tasks like calligraphy on a 2D plane to complex actions requiring precise movements and force interactions. However, the learning process can be significantly improved with the guidance of a coach who provides expert corrections and instructions. This combination of expert systems and reinforcement learning with human feedback can expedite the learning process for robots.

When it comes to robotics control, the distinction between high-level and low-level control is essential. High-level control refers to task-level instructions, whereas low-level control pertains to skill-level instructions. Most current embodied models output discrete target positions without considering factors like smooth trajectory, optimal timing, or energy efficiency. While VoxPoser introduces path generation concepts, further exploration is required for trajectory planning. Additionally, real-time control is limited to 1-5Hz, which falls short of the strict definition of real-time systems in robotics.

To address these challenges, there are a few actionable pieces of advice to consider:

1. Incorporate real-world physics knowledge: Enhancing the models with a deeper understanding of real-world physics can improve their ability to reason and generate instructions that are practical and applicable in physical robot environments.

2. Integrate expert guidance: Combining large models with expert systems and reinforcement learning can provide robots with the necessary corrections and instructions to acquire specialized skills more efficiently.

3. Improve real-time control: Investing in research and development to achieve higher control frequencies and real-time responsiveness is crucial for the smooth and precise movements of robots.

In conclusion, navigating the high cost of AI compute in the context of robot models requires a holistic approach. By addressing the limitations of current models, incorporating real-world knowledge, and leveraging expert guidance, we can enhance the capabilities of robots and bridge the gap between large-scale language models and the requirements of practical robot scenarios. Additionally, improving real-time control will ensure that robots can perform tasks with precision and efficiency. With these advancements, we can pave the way for a new era of intelligent and capable robots.