15 accepted papers
LiDAR depth cameras are widely used for accurate depth measurement in various applications. However, when multiple cameras operate simultaneously, mutual interference causes artifacts in the captured depth data, which existing image restoration methods struggle to handle. In this paper, we propose D…
Deep reinforcement learning has proven to be a powerful approach to solving control tasks, but its characteristic high‑frequency oscillations make it difficult to apply in real‑world environments. While prior methods have addressed action oscillations via architectural or loss-based methods, the lat
Enabling robots to interact effectively with the real world requires extensive learning from physical interaction data, making simulation crucial for generating such data safely and cost-effectively. Despite the advantages of simulation, manual environment creation remains a laborious process, motiv…
Perspective-aware spatial reasoning involves understanding spatial relationships from specific viewpoints--either egocentric (observer-centered) or allocentric (object-centered).While vision-language models (VLMs) perform well in egocentric settings, their performance deteriorates when reasoning fro
Vision Transformers are widely adopted as the backbone of vision foundation models, but they are known to produce high-norm artifacts that degrade representation quality. When knowledge distillation transfers these features to students, high-norm artifacts dominate the objective, so students overfit…
Hand-eye calibration is a fundamental task in robotics, requiring high precision to ensure accurate manipulation. This is especially crucial for recent markerless methods, which depend on precise pose estimation for effective end-effector calibration. In this paper, we propose a novel approach that …
Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt to enforce smoothness by directly regularizing the policy's output. We argue that this approach treats the symptom rathe…
Monocular camera-LiDAR data fusion has demonstrated remarkable environmental perception capabilities in various fields. The success of data fusion relies on the accurate matching of correspondence features from images and point clouds. In this letter, we propose a target-based Camera-LiDAR extrinsic
LiDAR depth cameras are widely used for accurate depth measurement in various applications. However, when multiple cameras operate simultaneously, mutual interference causes artifacts in the captured depth data, which existing image restoration methods struggle to handle. In this paper, we propose D
Reinforcement learning (RL) has proven its potential in complex decision-making tasks. Yet, many RL systems rely on manually crafted state representations, requiring effort in feature engineering. Visual Reinforcement Learning (VRL) offers a way to address this challenge by enabling agents to learn…
As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their personal knowledge in trained models. Despite several research efforts to address this, they often fail to consider the real-world demand from users fo…
Visual Reinforcement Learning (RL) facilitates learning directly from raw images; however, the domain gap between training and testing environments frequently leads to a decline in performance within unseen environments. In this paper, we propose Fourier Guided Adaptive Adversarial Augmentation (FGA…
Hand-eye calibration is a fundamental task in robotics, requiring high precision to ensure accurate manipulation. This is especially crucial for recent markerless methods, which depend on precise pose estimation for effective end-effector calibration. In this paper, we propose a novel approach that
Deep Neural Networks (DNNs) have become pivotal in various fields especially in computer vision outperforming previous methodologies. A critical challenge in their deployment is the bias inherent in data across different domains such as image style and environmental conditions leading to domain gaps…
The 360<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> camera is a compact omnidirectional perception system for capturing panoramic images with the same field of view as LiDA