Real-time Motion Planning for Robotic Teleoperation Using Dynamic-goal Deep Reinforcement Learning

Kamali, Kaveh; Bonev, Ilian A.; Desrosiers, Christian

doi:10.1109/crv50864.2020.00032

Cited by 27 publications

(12 citation statements)

References 26 publications

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“…[110], Wang et al formulated autonomous driving as a hierarchical behaviour and motion-planning problem and trained the policy by PPO. Kamali et al [111] proposed a dynamic-goal deep RL method to address the problem of robot arm motion planning in tele-manipulation applications. Their method leverages PPO to train the policy network with the robot arm joint value and the reference trajectory.…”

Section: Motion Planning With Policy-based Rl Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A survey of learning‐based robot motion planning

Wang

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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A fundamental task in robotics is to plan collision-free motions among a set of obstacles. Recently, learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments. This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning-related definition of motion-planning problem are provided in this article. Different learning-based motion-planning algorithms are introduced, and the combination of classical motion-planning and learning techniques is discussed in detail.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Motion Planning With Policy-based Rl Methodsmentioning

confidence: 99%

“…Kamali et al. [111] proposed a dynamic‐goal deep RL method to address the problem of robot arm motion planning in tele‐manipulation applications. Their method leverages PPO to train the policy network with the robot arm joint value and the reference trajectory.…”

Section: Reinforcement Learning Based Motion Planningmentioning

confidence: 99%

A survey of learning‐based robot motion planning

Wang

Zhang

et al. 2021

IET Cyber-Syst and Robotics

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“…Instead of directly tracking an operator's arm pose, refs. [34,35] both use machine learning techniques to learn a model that will map user input to robot motion in VR teleoperation interfaces. This way the only input required from the operator is the desired end-effector pose, which a handheld VR controller can supply, but still provide efficient teleoperation.…”

Section: Robot Control and Planningmentioning

confidence: 99%

A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots

Wonsick

Padır

2020

Applied Sciences

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There is a significant amount of synergy between virtual reality (VR) and the field of robotics. However, it has only been in approximately the past five years that commercial immersive VR devices have been available to developers. This new availability has led to a rapid increase in research using VR devices in the field of robotics, especially in the development of VR interfaces for operating robots. In this paper, we present a systematic review on VR interfaces for robot operation that utilize commercially available immersive VR devices. A total of 41 papers published between 2016–2020 were collected for review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Papers are discussed and categorized into five categories: (1) Visualization, which focuses on displaying data or information to operators; (2) Robot Control and Planning, which focuses on connecting human input or movement to robot movement; (3) Interaction, which focuses on the development of new interaction techniques and/or identifying best interaction practices; (4) Usability, which focuses on user experiences of VR interfaces; and (5) Infrastructure, which focuses on system architectures or software to support connecting VR and robots for interface development. Additionally, we provide future directions to continue development in VR interfaces for operating robots.

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“…Neural Network-based approaches have been utilised to predict the master side (human side) intent [72]- [74]. Neural network-based methods have also been used in reinforcement learning [75], [76] and inverse reinforcement learning methods (including deep imitation learning) [77]- [80]. Methods have also been proposed to predict the force feedback signal from the end of the robot [81].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Prediction of Deterministic Human Intent Signal to augment Haptic Glove aided Control of Robotic Hand

Kumar,

Gandotra,

Lall

et al. 2021

Preprint

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The paper focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation. A control algorithm is presented to allow the RH relocate the object held to a goal pose. The motion signals for both the HG and the RH are high dimensional. The RH kinematics is usually different from the HG kinematics. The variability of kinematics of the two devices, added with the incomplete information about the human hand kinematics result in difficulty in direct mapping of the high dimensional motion signal of the HG to the RH. Hence, a method is proposed to estimate the human intent from the high dimensional HG motion signal and reconstruct the signal at the RH to ensure object relocation. It is also shown that the lag in synthesis of the motion signal of the human hand added with the control latency of the RH leads to a requirement of the prediction of the human intent signal. Then, a recurrent neural network (RNN) is proposed to predict the human intent signal ahead of time.

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Real-time Motion Planning for Robotic Teleoperation Using Dynamic-goal Deep Reinforcement Learning

Cited by 27 publications

References 26 publications

A survey of learning‐based robot motion planning

A survey of learning‐based robot motion planning

A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots

Estimation and Prediction of Deterministic Human Intent Signal to augment Haptic Glove aided Control of Robotic Hand

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