Mobile Robot Navigation With Hierarchical Policy Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robot navigation systems face challenges in deploying reinforcement learning-based policy models on real robots due to high sample complexity, leading to poor performance in dynamic environments, as they are often trained in simulation environments with different observation data.
Innovation Solution
The implementation of a dual policy model approach, where a high-level recurrent neural network model handles long-range planning and a low-level feed-forward neural network model generates granular actions for obstacle avoidance and efficient movement, trained using supervised learning for the high-level model and reinforcement learning for the low-level model, allowing for cooperative navigation without relying on environmental maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is used to train policy models for robot navigation, then the robot can learn to associate raw observations with actions without mapping or explicit path planning, but the high sample complexity causes the models to fail when deployed on real robots due to visual differences between simulation and real sensor data
Solution Approach 1:
The navigation system is segmented into two distinct policy models: a high-level policy model for long-range path planning and a low-level policy model for immediate obstacle avoidance and precise control. This segmentation allows each model to be trained for its specific function, with the high-level model handling strategic decisions and the low-level model handling tactical responses to real-time sensor data, thereby resolving the simulation-to-reality transfer problem.
Solution Approach 2:
The high-level policy model acts as an intermediary that generates coarse navigation paths based on target locations, which then guide the low-level policy model. The low-level model receives both the high-level path guidance and real-time sensor observations, mediating between the strategic plan and the actual robot control commands. This intermediary structure allows the system to benefit from both simulated training data for high-level planning and real-world sensor data for low-level execution.
2Device complexity
If a single policy model is used for both long-range planning and precise control, then the system structure is simpler, but the model cannot simultaneously handle both strategic navigation and reactive obstacle avoidance effectively
Solution Approach 1:
The navigation system is segmented into two distinct policy models: a high-level policy model for long-range path planning and a low-level policy model for immediate obstacle avoidance and precise control. This segmentation allows each model to be trained for its specific function, with the high-level model handling strategic decisions and the low-level model handling tactical responses to real-time sensor data, thereby resolving the simulation-to-reality transfer problem.
Solution Approach 2:
The system dynamically switches between high-level and low-level policy model control based on the situation. The high-level model provides strategic guidance during stable navigation phases, while the low-level model takes over for reactive control when obstacles are detected or precise positioning is needed. This dynamic allocation of control authority optimizes navigation efficiency while maintaining system simplicity.
3Adaptability or versatility
If reinforcement learning with high sample complexity is used for training, then the model can learn complex navigation behaviors, but the training requires extensive computational resources and time
Solution Approach 1:
The navigation system is segmented into two distinct policy models: a high-level policy model for long-range path planning and a low-level policy model for immediate obstacle avoidance and precise control. This segmentation allows each model to be trained for its specific function, with the high-level model handling strategic decisions and the low-level model handling tactical responses to real-time sensor data, thereby resolving the simulation-to-reality transfer problem.
Solution Approach 2:
The high-level policy model is trained in advance using simulated data to learn long-range navigation strategies and path planning. This preliminary training in simulation provides a robust foundation that reduces the amount of real-world training data needed. The low-level policy model then focuses on learning reactive behaviors from real sensor data, significantly reducing overall training time and computational requirements compared to training a single comprehensive model from scratch.
Data Source
AI summary
Training and/or using both a high-level policy model and a low-level policy model for mobile robot navigation. High-level output generated using the high-level policy model at each iteration indicates a corresponding high-level action for robot movement in navigating to the navigation target. The low-level output generated at each iteration is based on the determined corresponding high-level action for that iteration, and is based on observation(s) for that iteration. The low-level policy model is trained to generate low-level output that defines low-level action(s) that define robot movement more granularly than the high-level action—and to generate low-level action(s) that avoid obstacles and/or that are efficient (e.g., distance and/or time efficiency).