Task-agnostic Policy Filter for Safe Autonomous Control
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Solution Overview
Problem
Autonomous agents, such as vehicles and robots, face safety issues due to unexpected behavior when trajectories diverge from the training data distribution, leading to potential unsafe actions, particularly in high-dimensional perception environments where distributional shift occurs.
Innovation Solution
A task-agnostic policy filter control system is developed, utilizing neural ordinary differential equations (ODE) and in-distribution barrier functions (iDBFs) to encode observations and actions, computing learned latent state-space dynamic models, and inferring iDBF values to ensure the system remains within the distribution of offline-collected safe demonstrations, thereby preventing unsafe control inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If learning-based control approaches are used to perform complex tasks from high-dimensional perception data, then task performance and adaptability are improved, but safety deteriorates when trajectories diverge from training data distribution
Solution Approach 1:
The patent introduces a control filter as an intermediary component that wraps around the learned controller. This filter acts as a mediator between the learning-based controller and the physical system, monitoring trajectories and intervening when they diverge from the training distribution. The filter uses barrier functions to detect unsafe states and modifies control inputs to keep the system within safe boundaries, thus resolving the contradiction between adaptive task performance and safety reliability.
Solution Approach 2:
The patent performs preliminary actions by pre-training the controller on a dataset of safe demonstrations and pre-computing barrier functions that define safe operating regions before actual operation. The control filter is pre-configured with these barrier functions to proactively prevent unsafe behavior before it occurs, rather than reacting after safety violations happen. This preliminary preparation ensures safety is built-in from the start while maintaining task performance.
2Adaptability or versatility
If the system operates outside the training data distribution to handle novel situations, then adaptability improves, but unexpected unsafe behavior increases
Solution Approach 1:
The patent implements continuous feedback through the control filter that monitors the system state at each time step. The barrier functions provide real-time feedback about whether the current trajectory remains within the safe distribution. When the system approaches unsafe regions or diverges from training distribution, the filter receives feedback and automatically adjusts control inputs to redirect the system back into safe operating regions, preventing unexpected unsafe behavior while allowing controlled exploration.
3Reliability
If a control filter is introduced to ensure safety by keeping trajectories in-distribution, then safety improves, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical or rule-based safety verification systems with a learning-based control filter that uses neural network-derived barrier functions. Instead of implementing intricate safety checklists or manual verification procedures, the system substitutes these with a compact neural network model that automatically learns safe operating boundaries from demonstration data. This substitution maintains high safety standards while reducing overall system complexity and improving computational efficiency.
Data Source
AI summary
A method for a task-agnostic policy filter control system is described. The method includes encoding a current observation, a previous latent state, and a previous action to output a new latent state. The method also includes computing, by a neural ordinary differential equations (ODE) module, learned latent state-space dynamic models for the new latent state. The method further includes inferring, by an in-distribution barrier function (iDBF) model, an iDBF value in response to the new latent state. The method also includes computing, based on the learned latent state-space dynamic models, the iDBF value and a reference control input for a current timestep, and a current action. The current action keeps the task-agnostic policy filter control system in-distribution with respect to an offline-collected dataset of safe demonstrations.