Tesla Optimus 3 Software System Upgrade: Key Directions
$Tesla(TSLA.US) Tesla’s Optimus 3 robot is getting a major software upgrade, and it’s not just about making the bot smarter—it’s also opening up fresh opportunities for A-share listed suppliers in the value chain. Here’s my breakdown of the main areas where the software system is evolving, plus which companies and partners stand to benefit.
🧠 Main Directions for the Software Upgrade
The Optimus 3 software upgrade is a huge step toward making it a true “general-purpose AI carrier.” Here’s what’s changing:
Multimodal Perception & Fusion Algorithms:
Optimus 3 has to make sense of massive data streams from all kinds of sensors—cameras for vision, touch sensors, force sensors, joint encoders, and more. The software needs to efficiently fuse these different data sources to build a unified, accurate view of the environment. For example, when the robot’s dexterous hand threads a needle, it combines visual positioning with fingertip touch feedback to fine-tune its movements.
Video Learning & Imitation Algorithms:
Tesla is moving away from motion capture suits and remote control, relying more on video data to train the robot. This means the software has to extract key action sequences from human demonstration videos, understand task intent, and generalize learned skills to new scenarios. It’s all about powerful computer vision and representation learning.
End-to-End Neural Network Control:
Tesla is pushing for end-to-end neural networks that map sensor inputs directly to joint control commands. This approach cuts out traditional control chain steps and manual rule design, making the robot more adaptable and responsive in complex, unstructured environments. Optimus is already using these networks for factory tasks like battery sorting.
AI “World Model” & Reasoning/Planning:
Now that Ashok Elluswamy (formerly head of Tesla Autopilot) is leading the Optimus project, he might bring the proven “world model” concept from autonomous driving into robot training. This lets the AI predict how its actions will affect the environment and plan ahead safely—key for real autonomy.
Large-Scale Simulation & Reinforcement Learning:
Training the robot in virtual environments with massive numbers of simulated tasks, then refining its strategies through reinforcement learning, is a cost-effective way to boost capabilities. This is similar to how Tesla uses the Dojo supercomputer to train its FSD system.
System Integration & Optimization:
With more powerful AI chips coming (like the anticipated AI5 chip with 40x performance), the software needs to maximize hardware capabilities—think low-level drivers, task scheduling, power consumption, and heat management.
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