Hierarchical Robot Controller for Safe Latent Action Transfer
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Solution Overview
Problem
Existing machine learning models for controlling robots often memorize latent action sequences during training, leading to unsafe control inputs in real-world scenarios, such as high torques and jerk, which can cause wear and tear or hardware failures.
Innovation Solution
A hierarchical controller architecture using a task policy neural network and a low-level controller neural network that maps latent action vectors to safe and effective control inputs, trained through imitation and reinforcement learning, allowing for versatile and robust motor skills transfer from simulation to real-world robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a machine learning model is trained to control a robot, then the robot can perform tasks autonomously, but the model may memorize latent action sequences and generate unsafe control inputs such as high torques and jerk
Solution Approach 1:
The controller is segmented into two distinct neural networks: a task policy neural network that generates latent action vectors for high-level task planning, and a low-level controller neural network that maps these latent vectors to safe control inputs. This segmentation allows each network to specialize in different aspects of control, with the low-level network specifically trained to ensure safety constraints are met while maintaining autonomous task performance.
2Productivity
If a controller is trained in simulation, then training can be performed efficiently and safely, but the controller may not generalize well to real-world robots
Solution Approach 1:
Latent action vectors serve as an intermediary representation between the simulation-trained task policy and the real-world robot control. The task policy neural network is trained in simulation to generate latent action vectors, which are then processed by the low-level controller neural network to produce safe control inputs for the real robot. This intermediary approach allows efficient simulation-based training while ensuring reliable real-world performance through the safety-guaranteeing low-level controller.
3Adaptability or versatility
If the controller learns diverse motor skills, then the robot can perform multiple tasks, but the control inputs may become complex and difficult to regulate for safety
Solution Approach 1:
The low-level controller neural network extracts and separates the safety-critical aspects of control from the task-specific latent action vectors. By taking out the safety regulation function and embedding it specifically in the low-level controller, the system can learn diverse motor skills through the task policy network while the low-level network ensures all control inputs meet safety constraints, regardless of task complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using a hierarchical controller that includes a task policy neural network and a low-level controller neural network.