Robot Control Policy Training with Pose Estimation Errors
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Solution Overview
Problem
Construction sites present unpredictable and unstructured environments, making it challenging to train robot control policies effectively due to errors in pose estimation, which are exacerbated by noisy sensor measurements and varied tasks, hindering automation efforts.
Innovation Solution
A method is developed to train robot control policies by generating disturbed observations based on pose estimation errors, using expert knowledge to create realistic training data that accounts for sensor noise, allowing the robot to perform correctly even with erroneous pose estimates, and incorporating domain adaptation techniques to bridge the simulation-to-real gap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard training methods for autonomous vehicles are used in construction sites, then the robot can perform tasks in structured environments, but the robot fails to handle pose estimation errors and sensory noise in unstructured environments
Solution Approach 1:
The patent applies preliminary action by generating disturbed observations with pose estimation errors and sensory noise before actual deployment. The training data is pre-augmented with realistic disturbances including position errors, orientation errors, and sensor noise to prepare the robot for unstructured construction site environments before it encounters real uncertainties during operation.
Solution Approach 2:
The patent implements parameter changes by systematically varying observation parameters including position offsets, orientation angles, and sensor measurement noise levels. These parameter variations create diverse training scenarios that expose the robot to a wide range of pose estimation errors and sensory disturbances, enabling it to learn robust control policies that generalize to unstructured environments.
2Measurement precision
If data collection for training is performed in real construction sites, then the training data reflects real uncertainties and errors, but safety, time, and costs increase significantly
Solution Approach 1:
The patent applies copying by creating synthetic training data that replicates the characteristics of real construction site observations. Instead of collecting data in actual construction sites, the system generates virtual observations with pose estimation errors and sensory noise that mirror real-world conditions, achieving realistic training data without the associated safety risks, time consumption, and costs.
Solution Approach 2:
The patent introduces an intermediary simulation environment that bridges the gap between controlled lab settings and complex real construction sites. This intermediary system generates training data with realistic uncertainties and errors without requiring physical deployment in hazardous construction environments, thus avoiding safety issues while maintaining measurement precision.
3Reliability
If the robot is trained to be robust against pose estimation errors, then the control policy performs well under uncertainty, but the training complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by breaking down the complex training process into distinct components: generating pose estimation errors, adding sensory noise, creating disturbed observations, and training the control policy. This modular approach to training data generation simplifies the overall complexity by allowing each component to be developed and optimized independently while maintaining systematic control over the training process.
Data Source
AI summary
A method for training a control policy for a robot device includes acquiring a reference state of an environment of the robot device and a reference observation of the environment for the reference state. The method also includes generating, for each of a plurality of errors of an estimation of a pose of the robot device, an observation that is disturbed with respect to the reference observation according to the error of the pose estimation and a training data element comprising the generated observation as a training input. The method further includes training the control policy using the generated training data elements.