Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing

Yang, Max; Lin, Yijiong; Church, Alex; Lloyd, John W.; Zhang, Dandan; Barton, David A. W.; Lepora, Nathan F.

doi:10.1109/lra.2023.3295236

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing [90] Lack of tactile sensing.…”

Section: Hierarchical Reinforcement Learning Integrating With Humanmentioning

confidence: 99%

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

Ozalp,

Ucar,

Guzelis

2024

IEEE Access

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This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipulation with uncertain rewards, multitasking, transfer learning, multimodal, and Human-Robot Interaction (HRI). The articles are summarized in terms of the main contributions, methods, challenges, and highlights of the latest and relevant studies using DRL and IRL for robotic manipulation. Additionally, summary tables regarding the problem and solution are presented. The literature review then focuses on the concepts of trustworthy AI, interpretable AI, and explainable AI (XAI) in the context of robotic manipulation. Moreover, this review provides a resource for future research on DRL/IRL in trustworthy robotic manipulation.

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“…Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing [90] Lack of tactile sensing.…”

Section: Hierarchical Reinforcement Learning Integrating With Humanmentioning

confidence: 99%

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

Ozalp,

Ucar,

Guzelis

2024

IEEE Access

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Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Alessi,

Bianchi,

Stano

et al. 2024

Advanced Intelligent Systems

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Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

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Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing

Cited by 2 publications

References 22 publications

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

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