Currently, the programming of multi robot control systems is difficult and requires high requirements for robot terminals. In response to the drawbacks of complex programming and easy interruption of message redundanc...
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In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspa...
In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.
The energy crisis and extreme weather conditions are the growing concerns of decision makers and policymakers. Many sectors heavily depend on energy, including buildings, industries, transportation, and agriculture. A...
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High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantificat...
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Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many ...
ISBN:
(纸本)9798331314385
Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.
In the pursuit of developing autonomous agents capable of learning optimal behaviors, reinforcement learning has emerged as a foundational frame-work. This paper introduces a novel approach for state definition in dee...
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To assist doctors in cardiovascular intervention, the use of robots in surgery can effectively separate interventionalists from X-ray radiation, prevent the spread of respiratory diseases, perform remote operations to...
To assist doctors in cardiovascular intervention, the use of robots in surgery can effectively separate interventionalists from X-ray radiation, prevent the spread of respiratory diseases, perform remote operations to alleviate the regional imbalance of medical resources and address some procedural challenges. However, a lack of force feedback in human computer interaction of current surgical robots affects surgical efficiency and safety. In cardiovascular surgery, the catheter bends and twists as it is the dynamic multipoint line contact with the vessel. It is difficult to accurately model and register it. In addition, time delay affects system transparency. This paper proposes a multi-information fuzzy fusion approach that is based on medical experience in order to predict position and orientation online. More specifically, Fitts law is developed with a two-motion collaborative to estimate the surgical movement time. Experimental results reveal that the proposed method can result in high system transparency in robot-assisted cardiovascular surgery. It could further improve accuracy and security in surgical procedures.
Efficient and robust design of legged robots for diverse terrains remains a critical focus in robotics research. To streamline this process we present varyMorph, an open-source tool that programmatically generates div...
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This study proposes a novel approach for low-impedance rendering in robots to ensure safe human-robot interaction. Low-impedance control enhances safety by enabling robots to respond flexibly to physical contact with ...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
This study proposes a novel approach for low-impedance rendering in robots to ensure safe human-robot interaction. Low-impedance control enhances safety by enabling robots to respond flexibly to physical contact with humans. However, real-world disturbances such as friction often necessitate high impedance, creating a trade-off between safety and task precision. To address this challenge, a modified disturbance observer (DOB)-based control framework is introduced, designed to prevent external contact forces from being treated as disturbances. Experimental results demonstrate significant improvements in impedance rendering performance under various conditions, ensuring precise and safe robot operations in dynamic shared environments.
In this paper we present a CNN-based Interface for the control of prosthetic and robotic hand: a CNN visual system is trained with a set of images of daily life object in order to classify and recognize them. Such a c...
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