Seokju Lee

Text-guided non-rigid image editing involves complex edits for input images, such as changing motion or compositions of the object (e.g., making a horse jump or adding candles on a cake). Since it requires manipulating the structure of the object, existing methods often compromise “image identity”– defined as the overall object appearance and background details – particularly when combined with Stable Diffusion. In this work, we propose a new approach for non-rigid image editing with Stable Diffusion, aimed at improving the image identity preservation quality without compromising editability. Our approach comprises three stages: text optimization, latent inversion, and timestep-aware text injection sampling. Inspired by the success of Imagic, we employ their text optimization for smooth editing. Then, we introduce latent inversion to preserve the input image’s identity without additional model fine-tuning. To fully utilize the input reconstruction ability of latent inversion, we employ timestep-aware text injection sampling, strategically injecting the source text prompt in early sampling steps and then transitioning to the target prompt in subsequent sampling steps. This strategic approach seamlessly harmonizes with text optimization, facilitating complex non-rigid edits to the input without losing the original identity. We demonstrate the effectiveness of our method in terms of identity preservation, editability, and aesthetic quality through extensive experiments. Our code is available at https://github.com/YunjiJung0105/TOLI-non-rigid-editing

This study aims to enhance the accuracy of a six-axis force/torque (F/T) sensor by improving upon existing approaches that use a multilayer perceptron (MLP) and the least-squares method (LSM). While previous research has used MLPs for thermal compensation, it has not effectively addressed temperature-induced drift. The sensor used in this study operates based on infrared light and incorporates a photocoupler, which makes it highly sensitive to dark current effects, resulting in significant drift under temperature variations. Moreover, its compact and lightweight design (45 g) leads to low thermal capacity, making it susceptible to rapid temperature fluctuations even with minimal heat input, which in turn affects real-time performance. To address these challenges, this study proposes a gated recurrent unit (GRU)-based method and compares it with the conventional MLP approach. Experimental results demonstrate that the GRU-based model significantly reduces drift from 26 to 2.7 N, and the root mean square error (RMSE) from 500 to 3—representing a 100fold improvement. These findings suggest that GRU-based modeling substantially improves real-time F/T measurements in temperature-sensitive environments, benefiting applications in robotics and precision instrumentation.

Many studies on quadrupedal manipulators have been conducted for extending the workspace of the end-effector. Many of these studies, especially the recent ones, use model-based control for the arm and learning-based control for the leg. Some studies solely focused on model-based control for controlling both the base and arm. However, model-based controllers such as MPC can be computationally inefficient when there are many contacts between the end-effector and the object. The dynamics of the interactions between a quadrupedal manipulator and the object in contact are complex and often unpredictable without high-resolution contact sensors on the end-effector. In this study, we investigate the possibility of using a reinforcement learning strategy to control an end-effector of a legged mobile manipulator. The proposed framework is verified for a walking and tracking task of the end-effector in a simulation environment.