Tesla has announced major improvements to its humanoid robot, Optimus, which is now capable of picking up and sorting objects, doing yoga, and navigating through surroundings.
One of the key reasons for Optimus’s impressive capabilities is its use of neural networks. Neural networks are a type of machine learning that allows robots to learn and adapt to their environment. This is in contrast to rule-based systems, which are more limited in their abilities.
Optimus’s motion is facilitated by a sophisticated neural architecture that is trained in an end-to-end manner. This means that the robot takes in videos as input and produces actions as output.
To understand its surroundings, Optimus analyses images using efficient Vision Transformers (ViT) or more conventional backbone models like ResNet or EfficientNet. Videos can be processed in two ways—treating each frame as an individual image or considering the video as a whole. Different techniques, such as SlowFast Network or RubiksNet, are used to efficiently handle video data.
While it’s not entirely clear whether Optimus responds to language prompts, if it does, there’s a mechanism for integrating language with visual perception. Techniques like Feature-wise Linear Modulation (FiLM) may be employed for this purpose, allowing language embeddings to influence the image processing pathway.
To translate continuous motion signals into discrete actions that the robot can understand, Optimus might use various methods, such as categorising the movements or employing VQVAE for compression.
All these components work together within a Transformer-based controller. This controller takes in-video tokens (possibly modulated by language) and produces action tokens step-by-step. The robot continually refines its actions by observing the consequences of its previous moves, demonstrating its self-corrective abilities as seen in the demos.
The sources for this piece include an article in AnalyticsIndiaMag.