Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations

Benjamin, Remman, Sindre; Lekkas, Anastasios M.

doi:10.23919/ecc54610.2021.9654850

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…Replay and Shapley Additive Explanations [189] Problem of interpretability or human explainability of robot decision-making processes.…”

Section: Robotic Lever Manipulation Using Hindsight Experiencementioning

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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“…Replay and Shapley Additive Explanations [189] Problem of interpretability or human explainability of robot decision-making processes.…”

Section: Robotic Lever Manipulation Using Hindsight Experiencementioning

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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“…In the field of robotics, deep learning techniques and, specifically, deep reinforcement learning (DRL) [14], have been a true revolution, allowing robots to be endowed with both a great perceptive capacity and an advanced ability to learn through the exploration of the environment, with a strategy that the robot itself improves using trial and error techniques [21,22]. However, these techniques rely on models whose complexity prevents people from really understanding how decisions are being made or predictions carried out.…”

Section: Climbing the Ladder Of Causationmentioning

confidence: 99%

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

Marfil,

Bustos,

Bandera

2024

Electronics

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The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can benefit from advances in Automated Planning, allowing them to autonomously solve complex tasks. However, on many occasions, the robot still acts without internalising and understanding the reasons behind a perception or an action, beyond an immediate response to a current state of the context. This gap results in limitations that affect its performance, reliability, and trustworthiness. Deep learning alone cannot bridge this gap because the reasons behind behaviour, when it emanates from a model in which the world is a black-box, are not accessible. What is really needed is an underlying architecture based on deeper reasoning. Among other issues, this architecture should enable the robot to generate explanations, allowing people to know why the robot is performing, or has performed, a certain action, or the reasons that may have caused a certain plan failure or perceptual anomaly. Furthermore, when these explanations arise from a cognitive process and are shared, and thus validated, with people, the robot should be able to incorporate these explanations into its knowledge base, and thus use this understanding to improve future behaviour. Our article looks at recent advances in the development of self-aware, self-evolving robots. These robots are designed to provide the necessary explanations to their human counterparts, thereby enhancing their functional capabilities in the quest to gain their trust.

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“…Appendix A presents the detailed search results, methodology, and applied criteria. We could verify that many works in the literature apply well-known RL methods to approach different classes of complex and exciting problems such as: (i) geographical routing-decision process to assign sensing tasks to mobile users, (ii) anomaly detection in smart environments, (iii) cellular-connected unmanned aerial vehicles network, (iv) nonlinearities and uncertainties of biochemical reactions in wastewater treatment process control, (v) robotic lever control, (vi) handover decision in 5G Ultradense Networks, and (vii) automation of software test (Zhang and Qiu, 2022;Tao and Hafid, 2020;Fährmann et al, 2022;Koroglu and Sen, 2022;Crowder et al, 2021;Li et al, 2022b;Wu et al, 2022;Remman and Lekkas, 2021;Rosenbauer et al, 2020). However, we are more interested in those works that propose changes or new methods using Experience Replay.…”

Section: Research On Experience Replay and Some Directions For Future...mentioning

confidence: 99%

Advances and Challenges in Learning from Experience Replay

Neves

Patrocínio

Ishitani

2023

Preprint

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From the first theoretical propositions in the ’50s, inspired by the neu-roscience and psychology studies about the learning processes in humanbeings and animals, to its application in real-world problems on learn-ing to action, Reinforcement Learning (RL) is still being a fascinating,rich, and complex class of machine learning algorithms. In particular,we will start reviewing its fundamental principles and develop a discus-sion about how a technique called Experience Replay (ER) has beenof fundamental importance in making a variety of methods in most ofthe relevant problems and different domains more data efficient, usingagent experiences to improve its performance. We present some of themore relevant methods in the literature, which base most recent researchon improving RL with ER. Finally, we bring from the recent literaturesome of the main trends, challenges, and advances focused on reviewingand discussing ER formal basement and how to improve its propositionsto make it even more efficient in different methods and domains.

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Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations

Cited by 15 publications

References 12 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

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

Advances and Challenges in Learning from Experience Replay

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