A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

The robust balancing capability of humanoid robots against disturbances has been considered as one of the crucial requirements for their practical mobility in real-world environments. In particular, many studies have been devoted to the efficient implementation of the three balance strategies, inspired by human balance strategies involving ankle, hip, and stepping strategies, to endow humanoid robots with human-level balancing capability. In this paper, a robust balance control framework for humanoid robots is proposed. Firstly, a novel Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum (CAM) damping control over the time horizon of MPC to improve the balancing performance. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through extensive simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated, particularly in the presence of disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based CP controller that employs the ankle, hip, and stepping strategies. The supplementary video is available at https://youtu.be/CrD75UbYzdcComment: 19 pages,13 figure

Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection

Generative steganography is the process of hiding secret messages in generated images instead of cover images. Existing studies on generative steganography use GAN or Flow models to obtain high hiding message capacity and anti-detection ability over cover images. However, they create relatively unrealistic stego images because of the inherent limitations of generative models. We propose Diffusion-Stego, a generative steganography approach based on diffusion models which outperform other generative models in image generation. Diffusion-Stego projects secret messages into latent noise of diffusion models and generates stego images with an iterative denoising process. Since the naive hiding of secret messages into noise boosts visual degradation and decreases extracted message accuracy, we introduce message projection, which hides messages into noise space while addressing these issues. We suggest three options for message projection to adjust the trade-off between extracted message accuracy, anti-detection ability, and image quality. Diffusion-Stego is a training-free approach, so we can apply it to pre-trained diffusion models which generate high-quality images, or even large-scale text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high capacity of messages (3.0 bpp of binary messages with 98% accuracy, and 6.0 bpp with 90% accuracy) as well as high quality (with a FID score of 2.77 for 1.0 bpp on the FFHQ 64$\times$64 dataset) that makes it challenging to distinguish from real images in the PNG format

Proprioceptive External Torque Learning for Floating Base Robot and its Applications to Humanoid Locomotion

The estimation of external joint torque and contact wrench is essential for achieving stable locomotion of humanoids and safety-oriented robots. Although the contact wrench on the foot of humanoids can be measured using a force-torque sensor (FTS), FTS increases the cost, inertia, complexity, and failure possibility of the system. This paper introduces a method for learning external joint torque solely using proprioceptive sensors (encoders and IMUs) for a floating base robot. For learning, the GRU network is used and random walking data is collected. Real robot experiments demonstrate that the network can estimate the external torque and contact wrench with significantly smaller errors compared to the model-based method, momentum observer (MOB) with friction modeling. The study also validates that the estimated contact wrench can be utilized for zero moment point (ZMP) feedback control, enabling stable walking. Moreover, even when the robot's feet and the inertia of the upper body are changed, the trained network shows consistent performance with a model-based calibration. This result demonstrates the possibility of removing FTS on the robot, which reduces the disadvantages of hardware sensors. The summary video is available at https://youtu.be/gT1D4tOiKpo.Comment: Accepted by 2023 IROS conferenc

Multiple Goals, Attention Allocation, and the Intention-Achievement Gap in Energy Efficiency Innovation

Although improving energy efficiency has many benefits, including not only reducing pollution and climate change but also enhancing productivity and competitiveness, many firms still do not adopt energy efficiency innovation. In this study, we suggest inadequate attention allocation as a barrier to energy efficiency innovation, making firms fall into the intention-achievement gap when they simultaneously pursue multiple innovation-related goals. Due to limits in attention resources, competing innovation goals are likely to divert the firms’ focus of attention away from energy efficiency innovation, making them fail to achieve as much as they had initially intended. In addition, we argue that organizational innovation and government dependence will mitigate the attention dispersion effect of multiple goals by enhancing attention capacity and redirecting attention focus, respectively. We empirically examined our hypotheses in the context of Korean manufacturing industries between 2011 and 2013, using the Korean Innovation Survey 2014 data, and found supports for all hypotheses. In particular, we found that even a small increase in the diversity of innovation goals leads to a substantial likelihood of the intention-achievement gap and that organizational innovation and government dependence help to close the gap, but to a limited extent. Finally, theoretical contributions and practical implications are discussed

Nitrogen/argon diluted acetylene and ethylene blue flames under infrared CO2 laser irradiation

We investigated changes in emission spectra from nitrogen/argon diluted laminar diffusion acetylene and ethylene blue flames irradiated by a powerful cw infrared CO2 laser. The changes in the radical emission bands can be interpreted as an indication of laser-induced decomposition of ethylene (for laser absorbing C2H4 fuel) and of laser-absorbing intermediates (for non-absorbing C2H2 fuel). The results indicate that released active hydrogen plays an important role in addition/abstraction reactions without any participation of oxygen

MicroRNA miR-274-5p Suppresses <i>Found-in-Neurons</i> Associated with Melanotic Mass Formation and Developmental Growth in <i>Drosophila</i>

The hematopoietic system plays a crucial role in immune defense response and normal development, and it is regulated by various factors from other tissues. The dysregulation of hematopoiesis is associated with melanotic mass formation; however, the molecular mechanisms underlying this process are poorly understood. Here, we observed that the overexpression of miR-274 in the fat body resulted in the formation of melanotic masses. Moreover, abnormal activation of the JNK and JAK/STAT signaling pathways was linked to these consequences. In addition to this defect, miR-274 overexpression in the larval fat body decreased the total tissue size, leading to a reduction in body weight. miR-274-5p was found to directly suppress the expression of found-in-neurons (fne), which encodes an RNA-binding protein. Similar to the effects of miR-274 overexpression, fne depletion led to melanotic mass formation and growth reduction. Collectively, miR-274 plays a regulatory role in the fne–JNK signaling axis in melanotic mass formation and growth control

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area

Investigation of wound healing process guided by nano-scale topographic patterns integrated within a microfluidic system

<div><p>When living tissues are injured, they undergo a sequential process of homeostasis, inflammation, proliferation and maturation, which is called wound healing. The working mechanism of wound healing has not been wholly understood due to its complex environments with various mechanical and chemical factors. In this study, we propose a novel <i>in vitro</i> wound healing model using a microfluidic system that can manipulate the topography of the wound bed. The topography of the extracellular matrix (ECM) in the wound bed is one of the most important mechanical properties for rapid and effective wound healing. We focused our work on the topographical factor which is one of crucial mechanical cues in wound healing process by using various nano-patterns on the cell attachment surface. First, we analyzed the cell morphology and dynamic cellular behaviors of NIH-3T3 fibroblasts on the nano-patterned surface. Their morphology and dynamic behaviors were investigated for relevance with regard to the recovery function. Second, we developed a highly reproducible and inexpensive research platform for wound formation and the wound healing process by combining the nano-patterned surface and a microfluidic channel. The effect of topography on wound recovery performance was analyzed. This <i>in vitro</i> wound healing research platform will provide well-controlled topographic cue of wound bed and contribute to the study on the fundamental mechanism of wound healing.</p></div