49 accepted papers
Neural network–based visuomotor policies enable robots to perform manipulation tasks but remain susceptible to perceptual attacks. For example, conventional 2D adversarial patches are effective under fixed-camera setups, where appearance is relatively consistent; however, their efficacy often dimini…
Robotic manipulation involves kinematic and semantic transitions that are inherently coupled via underlying actions. However, existing approaches plan within either semantic or latent space without explicitly aligning these cross-modal transitions. To address this, we propose CLaD, a framework that
Visual Place Recognition (VPR) is a task of estimating the location of a query image, predominantly executed through image retrieval using learned global descriptors from a reference database of geo-tagged images. While recent approaches have aimed to improve the scalability of VPR training by lever
Cross-view multi-object tracking (CVMOT) aims to simultaneously track a group of objects over time in each view and to associate the same objects across different views. This task is essential for swarm monitoring and has attracted considerable attention in the literature. However, most existing dat
While 3D Gaussian splatting has emerged as a powerful paradigm, it fundamentally fails to model transparency such as glass panels. The core challenge lies in decoupling the intertwined radiance contributions from transparent interfaces and the transmitted geometry observed through the glass. We pres
Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping–plann
Latent diffusion models have achieved remarkable success in high-fidelity text-to-image generation, but their tendency to memorize training data raises critical privacy and intellectual property concerns. Membership inference attacks (MIAs) provide a principled way to audit such memorization by dete…
Skateboards offer a compact and efficient means of transportation as a type of personal mobility device. However, controlling them with legged robots poses several challenges for policy learning due to perception-driven interactions and multi-modal control objectives across distinct skateboarding ph…
Inverse rendering with Gaussian Splatting has advanced rapidly, but accurately disentangling material properties from complex global illumination effects, particularly indirect illumination, remains a major challenge. Existing methods often query indirect radiance from Gaussian primitives pre-traine…
Visual Place Recognition (VPR) has advanced significantly with high-capacity foundation models like DINOv2, achieving remarkable performance. Nonetheless, their substantial computational cost makes deployment on resource-constrained devices impractical. In this paper, we introduce an efficient asymm
Non-prehensile manipulation using onboard sensing presents a fundamental challenge: the manipulated object occludes the sensor's field of view, creating occluded regions that can lead to collisions. We propose CURA-PPO, a reinforcement learning framework that addresses this challenge by explicitly m…
Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However, existing RRT-based planners require explicit goal configurations specified as numerical joint angles, while many practical applications provide goal s
The outstanding capability of diffusion models in generating high-quality images poses significant threats when misused by adversaries. In particular, we assume malicious adversaries exploiting diffusion models for inpainting tasks, such as replacing a specific region with a celebrity. While existin…
We propose a hierarchical reinforcement learning (HRL) framework for efficient Navigation Among Movable Obstacles (NAMO) using a mobile manipulator. Our approach combines interaction-based obstacle property estimation with structured pushing strategies, facilitating the dynamic manipulation of unfor
Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (RL-ATR), inspired by humans' utilization of personal transporters, including Segways. The
Person search aims to simultaneously detect and re-identify a query person within an entire scene. While existing studies have made significant progress in achieving superior performance on clean datasets, the challenge of robustness under various corruptions remains largely unexplored. However, the…
Mobile robot navigation in crowded indoor environments is a challenging task due to the limited sensing capabilities of onboard sensors. In this study, we propose a mobile robot navigation framework that utilizes external CCTV data to address the limitations of local sensors in a crowded environment
Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in…
A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and resultant diverse inertia forces on the robot. To alleviate these challenges, we present the Learning-based Active Stabilizati…
LiDAR-camera calibration plays a crucial role in autonomous driving. However, operation-induced factors such as physical vibrations and temperature variations degrade the pre-deployment calibration accuracy, leading to the environmental perception performance deterioration. Recent recalibration meth…
We present a novel sound source localization method that leverages microphone pair training, designed to deliver robust performance in various real-world environments. Existing deep learning (DL)-based approaches face scalability issues when dealing with various types of microphone arrays. To addres
We present "SemCity" a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object synthetic indoor scenes or synthetic outdoor scenes while the generation of real-world outdoor scenes is rarely addressed. In this…
Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes. However existing methods select only informative and relevant views using predefined scores for training and testing phases. This constr…
Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activ…
Trajectory optimization (TO) is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional Cartesian path. The optimization performance largely depends on the quality of initial trajectories. However, the selection of a high-quality initial trajectory is non-t…
This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance r…
Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control…
This paper considers the problem of prolonged occlusions on navigation sensors due to dust, smudges, soils, etc. Such uncontrollable occlusions often cause lower visibility as well as higher uncertainty that require considerably sophisticated behavior. To secure visibility (i.e., confidence about th…
Humans can easily imagine a scene from auditory information based on their prior knowledge of audio-visual events. In this paper, we mimic this innate human ability in deep learning models to improve the quality of video inpainting. To implement the prior knowledge, we first train the audio-visual n…
Unsupervised person re-identification (re-ID) aims at learning discriminative representations for person retrieval from unlabeled data. Recent techniques accomplish this task by using pseudo-labels, but these labels are inherently noisy and deteriorate the accuracy. To overcome this problem, several…
In this letter, we present real-time collision-free inverse kinematics (RCIK) that accurately performs consecutively provided six-degrees-of-freedom commands in environments containing static and dynamic obstacles. Our method is based on an optimization-based IK approach to generate IK candidates wi
"A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset. However, obtaining ground-truth flows from a target video requires a tremendous effort. This paper proposes a practical fine tuning method to adapt a…
Vision aided dynamic exploration on bipedal robots poses an integrated challenge for perception and control. Rapid walking motions as well as the vibrations caused by the landing-foot contact-force introduce critical uncertainty in the visual-inertial system, which can cause the robot to misplace it…
Spatial verification is a crucial technique for particular object retrieval. It utilizes spatial information for the accurate detection of true positive images. However, existing query expansion and diffusion methods cannot efficiently propagate the spatial information in an ordinary graph with scal…
In this paper, we present a new approach, AKIMap, that uses an adaptive kernel inference for dense and sharp occupancy grid representations. Our approach is based on the multivariate kernel estimation, and we propose a simple, two-stage based method that selects an adaptive bandwidth matrix for an e…
Person following is an essential task for a robot to serve a person. In an indoor environment, however, the following task can be failed due to the occlusion of the target by structures, e.g., walls or pillars. To address this problem, we propose a method that helps the robot follow the target well…
We present a novel, robust sound source localization algorithm considering back-propagation signals. Sound propagation paths are estimated by generating direct and reflection acoustic rays based on ray tracing in a backward manner. We then compute the back-propagation signals by designing and using…
Recently, deep learning-based single image reflection separation methods have been exploited widely. To benefit the learning approach, a large number of training image pairs (i.e., with and without reflections) were synthesized in various ways, yet they are away from a physically-based direction. In…
A redundant manipulator has multiple inverse kinematics solutions per end-effector pose. Accordingly, there can be many trajectories for joints that follow a given end-effector path in the Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synth…
Deep unsupervised learning for optical flow has been proposed, where the loss measures image similarity with the warping function parameterized by estimated flow. The census transform, instead of image pixel values, is often used for the image similarity. In this work, rather than the handcrafted fe…
We present a novel sound localization algorithm for a non-line-of-sight (NLOS) sound source in indoor environments. Our approach exploits the diffraction properties of sound waves as they bend around a barrier or an obstacle in the scene. We combine a ray tracing-based sound propagation algorithm wi…
Mobile manipulation planning commonly adopts a decoupled approach that performs planning separately on the base and the manipulator. While this approach is fast, it can generate sub-optimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a high-dimensiona…
We present volumetric tree*, a hybridization of sampling-based and optimization-based motion planning. Volumetric tree* constructs an adaptive sparse graph with volumetric vertices, hyper-spheres encoding free configurations, using a sampling-based motion planner for a homotopy exploration. The coar…
A recent trend in optimal motion planning has broadened the research area toward the hybridization of sampling, optimization and grid-based approaches. We can expect that synergy from such integrations leads to overall performance improvement, but seamless integration and generalization is still an…
As personal autonomous mobility is getting to be more widely adopted, it is more important to consider comfortability of stuffs and persons carried by such mobility. In this work, we define the comfort of a trajectory as forces, specifically, translational force, received to objects carried by a rob…
We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame.…
We present an efficient anytime motion planner for mobile robots that considers both other dynamic obstacles and uncertainty caused by various sensors and low-level controllers. Our planning algorithm, which is an anytime extension of the Rapidly-exploring Random Belief Tree (RRBT), maintains the be…
In this paper, we introduce a novel shortlist computation algorithm for approximate, high-dimensional nearest neighbor search. Our method relies on a novel distance estimator: the residual-aware distance estimator, that accounts for the residual distances of data points to their respective quantized…
We present a novel approach, Super Ray, for efficiently updating map representations such as grids and octrees with point clouds. In this paper, we define a super ray for points as a representative ray to them with an associated frustum. A super ray is constructed in a way that updating those points