A Holistic Approach to Human-Supervised Humanoid Robot Operations in Extreme Environments

Wonsick, Murphy; Long, Philip; Onol, Aykut Ozgun; Wang, Maozhen; Padır, Taşkın

doi:10.3389/frobt.2021.550644

Cited by 12 publications

(2 citation statements)

References 75 publications

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“…Effective human-robot coordination is crucial to ensure that the interaction is transparent to the user. A supervised teleoperation scheme can allow the robot to follow human commands [1]- [3]. Similarly, the robotic agent of [4] assists in trajectory training for novices based on task-dependent reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Learning to Assist Bimanual Teleoperation Using Interval Type-2 Polynomial Fuzzy Inference

Wang

Fei

Huang

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Assisting humans in collaborative tasks is a promising application for robots, however effective assistance remains challenging. In this paper, we propose a method for providing intuitive robotic assistance based on learning from human natural limb coordination. To encode coupling between multiple-limb motions, we use a novel interval type-2 (IT2) polynomial fuzzy inference for modeling trajectory adaptation. The associated polynomial coefficients are estimated using a modified recursive least-square with a dynamic forgetting factor. We propose to employ a Gaussian process to produce robust human motion predictions, and thus address the uncertainty and measurement noise of the system caused by interactive environments. Experimental results on two types of interaction tasks demonstrate the effectiveness of this approach, which achieves high accuracy in predicting assistive limb motion and enables humans to perform bimanual tasks using only one limb.

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Section: Introductionmentioning

confidence: 99%

Learning to Assist Bimanual Teleoperation Using Interval Type-2 Polynomial Fuzzy Inference

Wang

Fei

Huang

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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“…We investigate the efficacy of our approach by conducting a human-robot interaction experiment where subjects teleoperate a robotic arm in VR to screw in a set of bolts (shown in Figure 1). The task is motivated by the potential advantages of deploying robots to utilize tools under extreme conditions, e.g., nuclear decommissioning (Wonsick et al, 2021b). Prior to this experiment, a pilot user study was also held to designate the most suitable VR interface for the task.…”

Section: Introductionmentioning

confidence: 99%

Motion Polytopes in Virtual Reality for Shared Control in Remote Manipulation Applications

et al. 2021

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In remote applications that mandate human supervision, shared control can prove vital by establishing a harmonious balance between the high-level cognition of a user and the low-level autonomy of a robot. Though in practice, achieving this balance is a challenging endeavor that largely depends on whether the operator effectively interprets the underlying shared control. Inspired by recent works on using immersive technologies to expose the internal shared control, we develop a virtual reality system to visually guide human-in-the-loop manipulation. Our implementation of shared control teleoperation employs end effector manipulability polytopes, which are geometrical constructs that embed joint limit and environmental constraints. These constructs capture a holistic view of the constrained manipulator’s motion and can thus be visually represented as feedback for users on their operable space of movement. To assess the efficacy of our proposed approach, we consider a teleoperation task where users manipulate a screwdriver attached to a robotic arm’s end effector. A pilot study with prospective operators is first conducted to discern which graphical cues and virtual reality setup are most preferable. Feedback from this study informs the final design of our virtual reality system, which is subsequently evaluated in the actual screwdriver teleoperation experiment. Our experimental findings support the utility of using polytopes for shared control teleoperation, but hint at the need for longer-term studies to garner their full benefits as virtual guides.

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An Efficiently Convergent Deep Reinforcement Learning-Based Trajectory Planning Method for Manipulators in Dynamic Environments

Wang

Yang

et al. 2023

J Intell Robot Syst

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A Holistic Approach to Human-Supervised Humanoid Robot Operations in Extreme Environments

Cited by 12 publications

References 75 publications

Learning to Assist Bimanual Teleoperation Using Interval Type-2 Polynomial Fuzzy Inference

Learning to Assist Bimanual Teleoperation Using Interval Type-2 Polynomial Fuzzy Inference

Motion Polytopes in Virtual Reality for Shared Control in Remote Manipulation Applications

An Efficiently Convergent Deep Reinforcement Learning-Based Trajectory Planning Method for Manipulators in Dynamic Environments

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