Dozer Grading Policy Learning for Noisy Real-World Conditions
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Solution Overview
Problem
Existing methods for automating dozer grading tasks in construction sites face challenges when transitioning from simulated environments to real-world scenarios, as policies learned in clean and simplified simulations fail to perform effectively in real-world conditions due to noise and inaccuracies.
Innovation Solution
A novel imitation learning method using deep neural networks is employed, where the agent is trained on noisy data to imitate an expert's actions, and behavioral cloning is applied with perturbed states to optimize policy robustness, combining baseline methods with simulation data to overcome real-world obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If policies are learned in clean and simplified simulated environments, then training efficiency and data collection are improved, but the policy fails catastrophically when facing real-world scenarios with noise and inaccuracies
Solution Approach 1:
The patent applies preliminary action by pre-training the policy in a clean simulation environment to establish baseline performance, then subsequently exposing the policy to noisy and imperfect simulated data before real-world deployment. This staged approach allows the system to first learn ideal behaviors efficiently, then gradually adapt to real-world imperfections, resolving the contradiction between training efficiency and policy robustness.
Solution Approach 2:
The patent employs parameter changes by systematically introducing noise and imperfections into the simulation data during the training process. By varying data quality parameters from clean to noisy conditions, the policy learns to generalize across different data qualities. This enables efficient initial training on clean data while building robustness to real-world variations through controlled parameter degradation.
2Ease of manufacture
If classic detection techniques are used for perception, then the system can detect edges and simple features, but it fails when facing real-world situations with occlusions and measurement noise
Solution Approach 1:
The patent replaces classic detection techniques with deep neural networks for perception tasks. DNNs learn complex feature representations from data and can handle occlusions and noise through their hierarchical feature extraction capabilities. This substitution maintains the simplicity of implementation (single network architecture) while dramatically improving detection accuracy in real-world conditions with imperfections.
3Ease of operation
If a dozer is manually operated by experienced drivers, then grading tasks can be performed with high skill, but there is a shortage of experienced drivers and manual operation is less efficient
Solution Approach 1:
The patent implements self-service by enabling the dozer to autonomously perform grading tasks through learned policies. The system observes the environment, makes decisions, and executes actions without human intervention. This automation eliminates dependence on scarce experienced drivers while maintaining high grading quality through sophisticated perception and decision-making algorithms, thereby resolving the contradiction between operation quality and operator availability.
Data Source
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AI summary
The invention relates to a computer-implemented method of executing a behavioural cloning algorithm for self-supervised learning using recorded samples for a grading task with a dozer.