Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation

Khader, Shahbaz Abdul; Yin, Hang; Falco, Pietro; Kragić, Danica

doi:10.1109/lra.2021.3111962

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…Policies for Stability-Guaranteed Manipulation [190] The traditional stability analysis of DRL becomes difficult due to the uninterpretable nature of neural network policies and unknown system dynamics.…”

Section: Learning Deep Energy Shapingmentioning

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: Learning Deep Energy Shapingmentioning

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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“…Performance improvement spans different domains although research generally focuses on accuracy [15], safety [16], robustness [17], and contact stability [18]. Other studies, such as [19,20], analyzed stability from a different perspective considering that any state trajectory must be bounded and tend to the target position required by the task. For this purpose, the authors shaped the exploration of the RL agent with a Lyapunov function.…”

Section: Contact-rich Manipulation Tasks: Assembly and Disassemblymentioning

confidence: 99%

Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment

et al. 2022

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The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker.

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“…In contrast, our proposed method based on free parameterizations provides the same scalability as GNNs without imposing any constraints on the weight matrices to guarantee closed-loop stability. Although previous work explored stable-by-design control based on mechanical energy conservation [34], [35], these methods are limited to specific systems (e.g., SE(3) dynamics). On the other hand, our approach applies to a wider range of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Universal Approximation Property of Hamiltonian Deep Neural Networks

Zakwan

d’Angelo

Ferrari-Trecate

2023

IEEE Control Syst. Lett.

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Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly simple scenarios. Parameterizing these policies using Neural Networks (NNs) can deliver good performance, but their sensitivity to small input changes can destabilize the closed-loop system. This paper addresses this issue for a network of nonlinear dissipative systems. Specifically, we leverage well-established port-Hamiltonian structures to characterize deep distributed control policies with closed-loop stability guarantees and a finite L2 gain, regardless of specific NN parameters. This eliminates the need to constrain the parameters during optimization and enables training with standard methods like stochastic gradient descent. A numerical study on the consensus control of Kuramoto oscillators demonstrates the effectiveness of the proposed controllers.

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Learning Deep Energy Shaping Policies for Stability-Guaranteed Manipulation

Cited by 11 publications

References 13 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

Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment

Universal Approximation Property of Hamiltonian Deep Neural Networks

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