Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning

Hansen, Emil Blixt; Andersen, Rasmus Eckholdt; Madsen, Steffen; Bøgh, Simon

doi:10.1109/sii46433.2020.9025873

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…A Bayesian Inverse Reinforcement Learning Fail (BIRLF) algorithm allows the agent to use successful and unsuccessful observations by taking advantage of failed demonstrations [154]. In [155], the DDPG and Principal Component Analysis (PCA) methods have been used to show how IRL can transfer task knowledge from a human expert to a robot in a dynamic environment. A method for applying demonstration learning using IRL has been presented in [131].…”

Section: -Active Irlmentioning

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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Section: -Active Irlmentioning

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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“…One promising solution is to use inverse reinforcement learning techniques, which learn reward functions from demonstrations or expert behaviour. By combining inverse reinforcement learning with deep reinforcement learning, robots can learn more effective control policies for deformable object manipulation tasks [116][117][118]123].…”

Section: End-to-end Learningmentioning

confidence: 99%

Deformable Object Manipulation in Caregiving Scenarios: A Review

Wang,

Zhu

2023

Machines

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This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care.

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A Novel Pose Estimation Method of Object in Robotic Manipulation Using Vision-Based Tactile Sensor

Zhao

Sun

Zhou

et al. 2021

Communications in Computer and Information Science

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Transferring Human Manipulation Knowledge to Robots with Inverse Reinforcement Learning

Cited by 6 publications

References 17 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

Deformable Object Manipulation in Caregiving Scenarios: A Review

A Novel Pose Estimation Method of Object in Robotic Manipulation Using Vision-Based Tactile Sensor

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