Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment r...
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ISBN:
(纸本)9798350323658
Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highest affordance (i.e. process-of-elimination strategy [1]), which ignores the dependency between the self-assessment results and the remaining untried actions. However, this dependency is important since the previous failures might help trim the remaining over-estimated actions. In this paper, we set to investigate this dependency by learning a failureaware policy. We propose two architectures for the failureaware policy by representing the self-assessment results of previous failures as the variable state, and leveraging recurrent neural networks to implicitly memorize the previous failures. Experiments conducted on three tasks demonstrate that our method can achieve better performances with higher task success rates by less trials. Moreover, when the actions are correlated, learning a failure-aware policy can achieve better performance than the process-of-elimination strategy.
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF) neural 3D scene representations t...
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ISBN:
(纸本)9798350323658
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (NeRF) neural 3D scene representations trained from collections of calibrated images. NeRF does not decompose illumination and color, so to make registration invariant to illumination, we introduce the concept of a "surface field" - a field distilled from a pre-trained NeRF model that measures the likelihood of a point being on the surface of an object. We then cast nerf2nerf registration as a robust optimization that iteratively seeks a rigid transformation that aligns the surface fields of the two scenes. We evaluate the effectiveness of our technique by introducing a dataset of pre-trained NeRF scenes - our synthetic scenes enable quantitative evaluations and comparisons to classical registration techniques, while our real scenes demonstrate the validity of our technique in real-world scenarios. Additional results available at: https://***
This paper proposes employing robotics and automation technology to manned spaceflight to manage repetitive activities for the crew. Current operations on the international Space Station (ISS) were analyzed to better ...
ISBN:
(纸本)9798350336702
This paper proposes employing robotics and automation technology to manned spaceflight to manage repetitive activities for the crew. Current operations on the international Space Station (ISS) were analyzed to better determine target tasks for automation. Crew tasks on the ISS are precisely planned by the ground control and planning teams and then monitored and documented. In this study, astronauts' tasks related to JAXA's Japanese Experimental Module were analyzed based on their task names and categorized to determine consistently occulting, repetitive types of work. Based on the categorized tasks' durations, three categories were anticipated for future automation: sample and equipment retrieval (swapping), logistics (cargo handling), and monitoring. This paper discusses automationmethods for each category based on JAXA's ground and in-orbit robotic research and development.
Malaria continues to pose a significant health challenge worldwide, especially in most of under developing countries where most often there is limited healthcare resources. A death toll of nearly a million every year ...
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Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instrument to efficiently synth...
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ISBN:
(纸本)9798350323658
Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instrument to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.
This research advances Industry 4.0 by optimizing IoT ontology models, crucial for enhancing industrial automation's efficiency and adaptability. Employing a mixed-methods approach that integrates a systematic lit...
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ISBN:
(纸本)9798350351866;9798350351859
This research advances Industry 4.0 by optimizing IoT ontology models, crucial for enhancing industrial automation's efficiency and adaptability. Employing a mixed-methods approach that integrates a systematic literature review, expert interviews, and case analyses, we identify and address significant gaps in existing IoT ontologies. Our findings reveal that improved interoperability, scalability, and adaptability in IoT ontologies can significantly elevate the robustness and efficiency of Industry 4.0 infrastructures. The study introduces targeted refinement strategies that promise to future-proof IoT ontologies, thereby facilitating more resilient and dynamic industrial automation environments. This work not only sheds light on the current state of IoT ontologies but also sets a forward-looking agenda for their continuous improvement in the evolving landscape of Industry 4.0.
This study develops interdisciplinary models and algorithms to enhance the coordination and autonomy of multiple robots in radioactive environments. By integrating dynamic radiation source behaviors into a robotic sim...
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In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the obj...
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ISBN:
(纸本)9798350323658
In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning and a Convolutional Neural Network (CNN), trained on simulated object-agnostic images, to promote those that better comply with the actual tactile images from the sensors. We use the selected sensors configurations to optimize over the space of 6D poses using a Gradient Descent-based approach. We finally rank the obtained poses by penalizing those that are in collision with the sensors. We carry out experiments in simulation using the DIGIT vision-based sensor with several objects, from the standard YCB model set. The results demonstrate that our approach estimates object poses that are compatible with actual object-sensor contacts in 87.5% of cases while reaching an average positional error in the order of 2 centimeters. Our analysis also includes qualitative results of experiments with a real DIGIT sensor.
In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accu...
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ISBN:
(纸本)9798350323658
In the context of robotics, accurate ground-truth positioning is the cornerstone for the development of mapping and localization algorithms. In outdoor environments and over long distances, total stations provide accurate and precise measurements, that are unaffected by the usual factors that deteriorate the accuracy of Global Navigation Satellite System (GNSS). While a single robotic total station can track the position of a target in three Degrees Of Freedom (DOF), three robotic total stations and three targets are necessary to yield the full six DOF pose reference. Since it is crucial to express the position of targets in a common coordinate frame, we present a novel extrinsic calibration method of multiple robotic total stations with field deployment in mind. The proposed method does not require the manual collection of ground control points during the system setup, nor does it require tedious synchronous measurement on each robotic total station. Based on extensive experimental work, we compare our approach to the classical extrinsic calibration methods used in geomatics for surveying and demonstrate that our approach brings substantial time savings during the deployment. Tested on more than 30km of trajectories, our new method increases the precision of the extrinsic calibration by 25% compared to the best state-of-the-art method, which is the one taking manually static ground control points.
The classification of celestial objects has traditionally relied on spectral analysis, which involves identifying unique patterns in the light emitted or absorbed by the objects. In this study, we present a novel appr...
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