Robot Motion Control With Latent Vector Fields for Obstacle Avoidance
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Solution Overview
Problem
Existing methods for controlling robot dynamics using neural networks struggle to provide stability guarantees and integrate obstacle avoidance effectively, especially in dynamic environments with unknown regions.
Innovation Solution
A method involving a neural contractive dynamical system (NCDS) using a variational autoencoder (VAE) with a diffeomorphic decoder to learn contractive dynamics in a low-dimensional latent space, combined with a modulation matrix for obstacle avoidance, ensures stability and safety by reshaping the vector field to avoid obstacles and unknown regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are used to learn robot dynamics from demonstrations, then the robot can efficiently model highly dynamic motions, but stability guarantees become hard to provide
Solution Approach 1:
The patent introduces a latent space as an intermediary representation between the ambient state space and the robot control system. By mapping states to a latent space with a learned metric, the system provides stability guarantees through contraction properties in the latent space while maintaining the ability to model complex dynamics. The latent space acts as a mediator that reconciles the flexibility of neural network modeling with the need for stability guarantees.
2Reliability
If stability of dynamical systems is ensured, then the robot can successfully reach targets in the face of perturbations, but obstacle avoidance becomes challenging
Solution Approach 1:
The patent merges obstacle avoidance with the stable dynamical system by integrating obstacle information directly into the latent space metric. The learned metric in the latent space encodes both stability properties and obstacle locations, allowing the vector field to naturally avoid obstacles while maintaining contraction. This combines two previously separate requirements into a unified framework.
3Reliability
If obstacle avoidance is integrated into stable dynamical systems, then safety is improved, but computational cost increases
Solution Approach 1:
The patent performs preliminary action by pre-training the latent space metric using demonstrations and obstacle information before actual robot operation. The metric learning phase captures stability and obstacle avoidance properties in advance, allowing the robot to benefit from these pre-computed properties during execution without real-time computational overhead. This separates the computationally intensive metric learning from the real-time control phase.
4Reliability
If unknown regions are avoided to ensure robust control, then safety is improved, but the robot's ability to explore new states is limited
Solution Approach 1:
The patent applies local quality by allowing the latent space metric to have different properties in different regions. In regions covered by demonstrations, the metric ensures contraction and stability. In unknown regions, the metric naturally expands, allowing exploration while still providing guidance toward demonstrated behaviors. This local differentiation resolves the contradiction between avoiding unknown regions and enabling exploration.
Data Source
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AI summary
According to various embodiments, a method for controlling a robot device is described, comprising providing demonstrations for movements of the robot device, wherein each demonstration demonstrates dynamics of the robot device by indicating a sequence of states of the robot device in an ambient space, encoding states of the robot device which the robot device traverses in the demonstrations to encoded states in a latent space by an encoding function which maps states from the ambient space to the latent space, determining a vector field in the latent space representing the demonstrated dynamics, generating a reshaped vector field by reshaping the vector field in the latent space, generating a vector field in the ambient space by mapping the reshaped vector field to ambient space according to the decoding function and controlling the robot device to follow the generated vector field in ambient space.