Validate on Sim, Detect on Real - Model Selection for Domain Randomization

Leibovich, Gal; Jacob, Guy; Endrawis, Shadi; Novik, Gal; Tamar, Aviv

doi:10.1109/icra46639.2022.9811621

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Real-world simulations are used to train robots so that they can transfer their behavior to the physical world, known as simulation-to-real (sim2real) [1]. An example is training a vacuum cleaner robot in various virtual simulation environments before placing it in a real-world setting, allowing it to learn to clean efficiently and avoid obstacles in a safe and controlled virtual environment.…”

Section: Introductionmentioning

confidence: 99%

“…An example is training a vacuum cleaner robot in various virtual simulation environments before placing it in a real-world setting, allowing it to learn to clean efficiently and avoid obstacles in a safe and controlled virtual environment. However, if the training does not include all possible real-world states, it can lead to incorrect behavior [1]. Thus, most sim2real techniques utilize domain randomization (DR) to generate myriads of simulated environments with randomized properties such as object location, rotation, and color.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, most sim2real techniques utilize domain randomization (DR) to generate myriads of simulated environments with randomized properties such as object location, rotation, and color. They offer enough variability that even unrepresented real-world states can appear to the robot as other learned variations, making it robust in new situations [1,2]. This allows a robot to execute tasks despite encountering objects with different colors or placements [2].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose a conceptual workflow that distinguishes five sequential stages of human intervention in robot training: (1) validating and improving real-world scans, (2) correcting virtual representations, (3) specifying application-specific object properties, (4) verifying and influencing simulation environment generation, and (5) verifying robot training. At each stage, we introduce opportunities for application experts to collaborate in enhancing the simulation environment generation for robot training.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Keep the Human in the Loop: Arguments for Human Assistance in the Synthesis of Simulation Data for Robot Training

Liebers,

Megarajan,

Auda

et al. 2024

MTI

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Robot training often takes place in simulated environments, particularly with reinforcement learning. Therefore, multiple training environments are generated using domain randomization to ensure transferability to real-world applications and compensate for unknown real-world states. We propose improving domain randomization by involving human application experts in various stages of the training process. Experts can provide valuable judgments on simulation realism, identify missing properties, and verify robot execution. Our human-in-the-loop workflow describes how they can enhance the process in five stages: validating and improving real-world scans, correcting virtual representations, specifying application-specific object properties, verifying and influencing simulation environment generation, and verifying robot training. We outline examples and highlight research opportunities. Furthermore, we present a case study in which we implemented different prototypes, demonstrating the potential of human experts in the given stages. Our early insights indicate that human input can benefit robot training at different stages.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%