Pixel-Based Robot MPC Using Latent Dynamics for Stable Trajectory Prediction
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Solution Overview
Problem
Existing machine learning-based approaches for robots to perform tasks are limited by model accuracy and robustness, especially in dynamic environments, leading to divergent predictions for future states and trajectories.
Innovation Solution
Training a latent robot dynamics model using unsupervised trajectories with a deep neural network architecture, combining an encoder, transition function, and decoder, and a reward function to predict future actions and trajectories from pixel-based observations, enabling few-shot learning and efficient action sequence evaluation in latent space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple state-space model is used to predict future states and trajectories, then the computational complexity is low, but the prediction accuracy diverges quickly from actual states
Solution Approach 1:
The patent transforms the prediction approach by changing the parameter representation from direct pixel-space predictions to latent-space predictions. The model compresses pixel observations into latent states through an encoder, performs transitions in this compressed space, and reconstructs future states. This parameter transformation maintains computational efficiency while significantly improving prediction accuracy by capturing essential dynamics in a lower-dimensional manifold.
Solution Approach 2:
The patent introduces a latent space dimension that bridges pixel observations and predicted states. Instead of operating directly in high-dimensional pixel space or simple state space, the model creates an intermediate latent representation layer. This dimensional transformation allows the system to capture complex dynamics without the computational burden of full pixel-space modeling, resolving the accuracy-complexity tradeoff.
2Duration of action of moving object
If predictions are made for multiple future time instances, then the planning horizon is extended, but the predicted state and trajectory diverge quickly from actual state
Solution Approach 1:
The patent applies preliminary compression of pixel observations into latent states before performing multi-step predictions. By encoding current and past pixel observations into compact latent representations that capture essential dynamics, the model establishes accurate initial conditions for future predictions. This preliminary action in latent space prevents divergence even over extended prediction horizons, as the compressed representations are more robust to prediction errors.
3Measurement precision
If full image generation is performed for action sequence evaluation, then the visual accuracy is high, but the computational time increases
Solution Approach 1:
The patent extracts only the essential latent state representations needed for action sequence evaluation, rather than generating full images. By performing evaluation in the compressed latent space and only generating images when necessary for final visualization or detailed analysis, the system achieves rapid action sequence comparison and selection. This extraction approach maintains visual accuracy for critical assessments while dramatically improving evaluation throughput.
Data Source
AI summary
Techniques are disclosed that enable model predictive control of a robot based on a latent dynamics model and a reward function. In many implementations, the latent space can be divided into a deterministic portion and stochastic portion, allowing the model to be utilized in generating more likely robot trajectories. Additional or alternative implementations include many reward functions, where each reward function corresponds to a different robot task.