Language-driven grasp detection is a fundamental yet challenging task in robotics with various industrial applications. This work presents a new approach for language-driven grasp detection that leverages lightweight ...
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ISBN:
(纸本)9798350377712;9798350377705
Language-driven grasp detection is a fundamental yet challenging task in robotics with various industrial applications. This work presents a new approach for language-driven grasp detection that leverages lightweight diffusion models to achieve fast inference time. By integrating diffusion processes with grasping prompts in natural language, our method can effectively encode visual and textual information, enabling more accurate and versatile grasp positioning that aligns well with the text query. To overcome the long inference time problem in diffusion models, we leverage the image and text features as the condition in the consistency model to reduce the number of denoising timesteps during inference. The intensive experimental results show that our method outperforms other recent grasp detection methods and lightweight diffusion models by a clear margin. We further validate our method in real-world robotic experiments to demonstrate its fast inference time capability.
Many robotic mechanical systems exhibit non-linearity and are frequently subject to uncertainties. This paper introduces a distinctive and practical robust control algorithm, developed using the Udwadia-Kalaba (UK) eq...
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ISBN:
(纸本)9798350385731;9798350385724
Many robotic mechanical systems exhibit non-linearity and are frequently subject to uncertainties. This paper introduces a distinctive and practical robust control algorithm, developed using the Udwadia-Kalaba (UK) equation. By defining the trajectory of the ideal system, the algorithm incorporates the servo constraints. The controller proposed in this paper is composed of three primary components. The first part of the controller provides an analytical expression for the binding force of the system under servo constraints. The second part of the controller addresses the issue of initial condition incompatibility within the system. The third part serves as a robust component to effectively compensate for the impact of uncertainties. Finally, the proposed robust control method is validated through MATLAB simulation on a SCARA robot, incorporating the system's dynamics model. The simulation results demonstrate the controller's superior dynamic performance when compared to alternative control algorithms.
Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis...
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ISBN:
(纸本)9798350384581;9798350384574
Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.
Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and ma...
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ISBN:
(纸本)9798350384581;9798350384574
Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and make trust-aware decisions. Consequently, there is an urgent need for a generalized approach to recognize human-robot trust. This study addresses this need by introducing an EEG-based method for trust recognition during human-robot cooperation. A human-robot cooperation game scenario is used to stimulate various human trust levels when working with robots. To enhance recognition performance, the study proposes an EEG Vision Transformer model coupled with a 3-D spatial representation to capture the spatial information of EEG, taking into account the topological relationship among electrodes. To validate this approach, a public EEG-based human trust dataset called EEGTrust is constructed. Experimental results indicate the effectiveness of the proposed approach, achieving an accuracy of 74.99% in slice-wise cross-validation and 62.00% in trial-wise cross-validation. This outperforms baseline models in both recognition accuracy and generalization. Furthermore, an ablation study demonstrates a significant improvement in trust recognition performance of the spatial representation. The source code and EEGTrust dataset are available at https://***/CaiyueXu/EEGTrust.
The problem of dynamic contact between robots and the physical environment has always been a hot research topic with great significance. In the process of rapid development of robotics technology, the field of medical...
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This paper explores a hybrid control system for the reactive autonomous navigation of ground vehicles tested on a differential drive robot, in an unknown environment featuring both static and dynamic scenarios, includ...
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ISBN:
(纸本)9798350394283;9798350394276
This paper explores a hybrid control system for the reactive autonomous navigation of ground vehicles tested on a differential drive robot, in an unknown environment featuring both static and dynamic scenarios, including a maze-like environment. The task comprises two primary functions: navigation and obstacle avoidance, managed by two sub-controllers. Neural networks, trained via supervised learning on lookup tables generated from Type-2 Sugeno fuzzy logic controllers, are implemented for real time experiments due to constraints on processing capability. This hybrid approach capitalises on the computational efficiency and potential generalisation of neural networks while preserving the interpretability inherent to fuzzy logic controls. The efficacy of the proposed controllers is demonstrated in both simulation and real-world experiments as well as with comparison to other methods.
A spoofing detection algorithm based on Tracking Loop Correlation Output (TLCO) is proposed to address the issues of limited applicability and significant fluctuations in detection performance of traditional detection...
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