Manipulation for self-Identification, and self-Identification for better manipulation

Hang, Kaiyu; Bircher, Walter G.; Morgan, Andrew S.; Dollar, Aaron M.

doi:10.1126/scirobotics.abe1321

Cited by 26 publications

(7 citation statements)

References 57 publications

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“…In the industrial world, manipulating heavy objects is a fairly common and familiar operation. Manual manipulation, on the other hand, is inconvenient [45]. In many real applications, autonomous devices may not give the requisite amount of freedom in object handling [46].…”

Section: Power Assist Robotmentioning

confidence: 99%

Power Assist Rehabilitation Robot and Motion Intention Estimation

Adeola-Bello¹,

Azlan²

2022

IJRCS

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This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot. In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

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Section: Power Assist Robotmentioning

confidence: 99%

Power Assist Rehabilitation Robot and Motion Intention Estimation

Adeola-Bello¹,

Azlan²

2022

IJRCS

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“…Recent work has proposed the self-identification of necessary parameters through exploratory hand-object interactions using an external camera and particle filtering [36].…”

Section: Related Workmentioning

confidence: 99%

Learning Haptic-based Object Pose Estimation for In-hand Manipulation with Underactuated Robotic Hands

Azulay¹,

Ben-David²,

Sintov³

2022

Preprint

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Unlike traditional robotic hands, underactuated compliant hands are challenging to model due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually performed based on visual perception. However, visual perception of the hand and object can be limited in occluded or partly-occluded environments. In this paper, we aim to explore the use of haptics, i.e., kinesthetic and tactile sensing, for pose estimation and in-hand manipulation with underactuated hands. Such haptic approach would mitigate occluded environments where line-of-sight is not always available. We put an emphasis on identifying the feature state representation of the system that does not include vision and can be obtained with simple and low-cost hardware. For tactile sensing, therefore, we propose a low-cost and flexible sensor that is mostly 3D printed along with the finger-tip and can provide implicit contact information. Taking a two-finger underactuated hand as a test-case, we analyze the contribution of kinesthetic and tactile features along with various regression models to the accuracy of the predictions. Furthermore, we propose a Model Predictive Control (MPC) approach which utilizes the pose estimation to manipulate objects to desired states solely based on haptics. We have conducted a series of experiments that validate the ability to estimate poses of various objects with different geometry, stiffness and texture, and show manipulation to goals in the workspace with relatively high accuracy.

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“…This visual simulator bears similarities to the notion of a body schema in cognitive sciences, though it lacks the multimodal aspects necessary for self-recognition, as discussed in prior research (33)(34)(35). In the field of robotics, there has been a growing interest in data-driven self-modeling techniques, ranging from kinematic modeling, exemplified by joint configuration estimation for in-hand manipulation (36), to more intricate 3-D full-body models (37). There is also a great depth of study in imitation learning or behaviour cloning, that avoids the need of an explicit model (38)(39)(40)(41).…”

Section: Introductionmentioning

confidence: 99%

Visuo-dynamic self-modelling of soft robotic systems

Monteiro,

Shi,

Wurdemann

et al. 2023

Preprint

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Soft robots exhibit complex nonlinear dynamics with large degrees of freedom, making their modeling and control challenging. Typically, reduced-order models in time or space are used due to these challenges. However, such simplified models limit control accuracy and restrict the motion repertoire of the soft robots. In this work, we introduce an end-to-end learning-based approach for fully dynamic modeling of any general robotic system that do not rely on predefined structures. The key idea is to learn dynamic models of the robot directly in the visual space. The generated models possess identical dimensionality to the observation space, resulting in models whose complexity is determined by the sensory system without explicitly decomposing the problem. To validate the effectiveness of our proposed method, we apply it to a fully soft robotic manipulator. We demonstrate its applicability in controller development through an open-loop optimization-based controller. Our work thus far provides the most comprehensive strategy for controlling a general soft robotic system, without constraints on the shape, properties, and dimensionality of the system. This enables us to derive a wide range of dynamic control tasks including shape control, trajectory tracking and obstacle avoidance using a model derived from just 90 minutes of real-world data.

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Manipulation for self-Identification, and self-Identification for better manipulation

Cited by 26 publications

References 57 publications

Power Assist Rehabilitation Robot and Motion Intention Estimation

Power Assist Rehabilitation Robot and Motion Intention Estimation

Learning Haptic-based Object Pose Estimation for In-hand Manipulation with Underactuated Robotic Hands

Visuo-dynamic self-modelling of soft robotic systems

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