Robot Control Policy Training Across Multi-Realism Simulations
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Solution Overview
Problem
Remotely controlling robots wirelessly from a cloud-based controller is challenging due to network latency and the high cost and difficulty of simulating realistic environments, which affects the accuracy and efficiency of robotic operations.
Innovation Solution
Implementing reinforcement learning algorithms that associate input information with varying levels of realism from simulation environments, including digital twins and network connectivity options, to modify parameters and improve control accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning is trained in highly realistic simulation environments, then control accuracy improves, but simulation cost and complexity increase
Solution Approach 1:
The simulation environment is segmented into multiple versions with different realism levels (highly realistic, moderately realistic, simplified). Each version serves a specific training phase, allowing the system to progress from simpler to more complex simulations without being overwhelmed by full realism from the start.
Solution Approach 2:
The system performs preliminary training in simplified simulations before progressing to highly realistic environments. This preliminary action in less complex settings establishes baseline policies that are then refined in more realistic simulations, reducing the overall burden of training in high-fidelity environments.
2Ease of operation
If cloud-based wireless control is implemented, then remote control capability improves, but network latency increases
Solution Approach 1:
Control policies are pre-trained and optimized in simulation environments before deployment. This preliminary training allows the robot to have pre-computed action strategies that reduce the need for real-time cloud computation, thereby mitigating the impact of network latency during actual operation.
Solution Approach 2:
The simulation environment acts as an intermediary that bridges the gap between cloud-based control and real-time robot operation. By training policies in simulation, the system creates a intermediary layer of pre-processed control knowledge that reduces dependency on real-time network communication.
3Productivity
If multiple simulation environments with different realism levels are used, then training efficiency improves, but system complexity increases
Solution Approach 1:
The training process is segmented across multiple simulation environments with varying realism levels. Each environment targets specific aspects of the control policy, allowing parallel or sequential training that improves overall efficiency while managing complexity through structured organization.
Solution Approach 2:
The multi-level simulation system serves multiple functions: simplified simulations for baseline policy learning, moderately realistic simulations for refinement, and highly realistic simulations for final validation. This multi-functionality justifies the added system complexity by providing comprehensive training benefits.
Data Source
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AI summary
Methods and systems for controlling a robot. In one aspect, the method (1300) comprises obtaining (s1302) first input information associated with a first simulation environment to which a first level of realism is assigned and obtaining (s1304) second input information associated with a second simulation environment to which a second level of realism is assigned. The first level of realism is different from the second level of realism. The method further comprises associating (s1306) the first input information with a first realism value representing the first level of realism; and associating (s1308) the second input information with a second realism value representing the second level of realism. The method further comprises modifying (s1310), based on the associated first input information and the associated second input information, one or more parameters of a machine learning (ML) process used for controlling the robot.