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99 Work/Market Research/General Robotics.md

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+---
+title: General Robotics
+created_date: 2024-10-24
+updated_date: 2024-10-24
+aliases:
+tags:
+---
+# General Robotics
+
+## Hardware
+
+## Software
+
+### AI and Machine Learning
+#### World Model
+https://www.1x.tech/discover/1x-world-model
+https://worldmodels.github.io/
+https://techterrain.substack.com/p/world-models-vs-kalman-filter?r=vudom&utm_campaign=post&utm_medium=web&triedRedirect=true
+
+> [!Quote]- World Model Definition by Yann LeCunn
+> Lots of confusion about what a world model is. Here is my definition:  
+Given:  
+> - an observation x(t)  
+> - a previous estimate of the state of the world s(t)  
+> - an action proposal a(t)  
+> - a latent variable proposal z(t)  
+>   
+> A world model computes:  
+> - representation: h(t) = Enc(x(t))  
+> - prediction: s(t+1) = Pred( h(t), s(t), z(t), a(t) )  
+> Where  
+> - Enc() is an encoder (a trainable deterministic function, e.g. a neural net)  
+> - Pred() is a hidden state predictor (also a trainable deterministic function).  
+> - the latent variable z(t) represents the unknown information that would allow us to predict exactly what happens. It must be sampled from a distribution or or varied over a set. It parameterizes the set (or distribution) of plausible predictions.  
+>   
+> The trick is to train the entire thing from observation triplets (x(t),a(t),x(t+1)) while preventing the Encoder from collapsing to a trivial solution on which it ignores the input.  
+>   
+> Auto-regressive generative models (such as LLMs) are a simplified special case in which  
+> 1. the Encoder is the identity function: h(t) = x(t),  
+> 2. the state is a window of past inputs  
+> 3. there is no action variable a(t)  
+> 4. x(t) is discrete  
+> 5. the Predictor computes a distribution over outcomes for x(t+1) and uses the latent z(t) to select one value from that distribution.  
+> The equations reduce to:  
+> s(t) = [x(t),x(t-1),...x(t-k)]  
+> x(t+1) = Pred( s(t), z(t) )  
+> There is no collapse issue in that case.
+
+### Traditional Control
+#### Model Predictive Control
+
+### Perception
+#### Kalman Filter
+
+## Companies
+- [Clearpath Robotics: Mobile Robots for Research & Development](https://clearpathrobotics.com/)