Latent-Space Predictive Control for Robot Trajectory Planning
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Solution Overview
Problem
Existing robot control systems face challenges in accurately predicting future states and trajectories due to lack of accuracy and robustness in modeling, especially in dynamic environments, leading to incorrect task performance.
Innovation Solution
Implementing a latent robot dynamics model using a deep neural network, trained with unsupervised and supervised data, combined with a reward function for model predictive control, allows robots to plan actions in a compact latent space, enabling fast evaluation of action sequences and few-shot learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple state-space model is used to predict robot states and trajectories, then the model structure is simple and training is efficient, but the prediction accuracy and robustness deteriorate in dynamic environments
Solution Approach 1:
The patent transforms the robot dynamics model from simple state-space representation to a latent space representation using deep neural networks. This parameter transformation enables the model to capture complex environmental dynamics while maintaining computational efficiency through the latent space abstraction.
Solution Approach 2:
The patent replaces traditional mechanical control approaches with a learned latent dynamics model. By substituting explicit physics-based models with a neural network-based latent space model, the system achieves better adaptability to dynamic environments while maintaining reasonable computational complexity.
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 quickly diverge from actual state and trajectory
Solution Approach 1:
The patent introduces a latent space as an intermediary representation between the observed robot states and the predicted future trajectories. This latent space acts as a mediator that captures the essential dynamics without the accumulation of prediction errors that occurs in direct multi-step predictions, thereby maintaining reliability over extended horizons.
3Reliability
If more training data is collected to improve model accuracy, then the model robustness improves, but the training time and computational resources increase
Solution Approach 1:
The patent extracts the essential dynamics of the robot system into a compact latent space representation. By taking out only the critical dynamic features into the latent space, the model achieves robustness with less training data compared to modeling the full state space, thereby reducing training time while maintaining reliability.
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.