DROPO: Sim-to-real transfer with offline domain randomization

Tiboni, Gabriele; Arndt, Karol; Kyrki, Ville

doi:10.1016/j.robot.2023.104432

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…This technique has been effectively used in Sim-to-Real transfers for numerous robotic tasks. In general, approaches involve introducing noise into dynamics [6] and imagery [35], employing model ensembles [36], incorporating adversarial noise [37], and evaluating simulation bias [38].…”

Section: State Of the Art Reviewmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Petryshyn,

Postupaiev,

Ben Bari

et al. 2024

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The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.

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Section: State Of the Art Reviewmentioning

confidence: 99%

Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Petryshyn,

Postupaiev,

Ben Bari

et al. 2024

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Online vs. Offline Adaptive Domain Randomization Benchmark

Tiboni

Arndt²,

Averta³

et al. 2023

Human-Friendly Robotics 2022

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Soft robots are becoming extremely popular thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modeling a daunting task, and in many cases only an approximated description is available. This challenge makes reinforcement learning (RL) based approaches inefficient when deployed on a realistic scenario, due to the large domain gap between models and the real platform. In this work, we demonstrate, for the first time, how Domain Randomization (DR) can solve this problem by enhancing RL policies with: i) a higher robustness w.r.t. environmental changes; ii) a higher affordability of learned policies when the target model differs significantly from the training model; iii) a higher effectiveness of the policy, which can even autonomously learn to exploit the environment to increase the robot capabilities (environmental constraints exploitation). Moreover, we introduce a novel algorithmic extension of previous adaptive domain randomization methods for the automatic inference of dynamics parameters for deformable objects. We provide results on four different tasks and two soft robot designs, opening interesting perspectives for future research on Reinforcement Learning for closed-loop soft robot control.

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