19 accepted papers
Collision detection is a fundamental problem in robotics, but handling collisions between non-convex objects remains challenging. A common approach for representing non-convex geometry is a signed distance function (SDF). Voxel-based SDF (VoxelSDF) enables fast distance queries but suffers from disc…
Robotic manipulation commonly involves pick-and-place tasks in which regrasp may be necessary for low-dexterity manipulators. Many existing approaches rely on sampling, which becomes inefficient when repeated regrasp is required in high-dimensional configuration spaces. We propose a modular planning…
This paper presents a framework designed to tackle a range of planning problems arise in manipulation, which typically involve complex geometric-physical reasoning related to contact and dynamic constraints. We introduce the Contact Factor Graph (CFG) to graphically model these diverse factors, enab
In this paper, we implement the GPU-accelerated subsystem-based Alternating Direction Method of Multipliers (SubADMM) for interactive simulation. The challenging objective for interactive simulations is to deliver realistic results under tight performance, even for large-scale scenarios. We aim to a
Narrow passage path planning is a prevalent problem from industrial to household sites, often facing difficulties in finding feasible paths or requiring excessive computational resources. Given that deep penetration into the environment can cause optimization failure, we propose a framework to ensur
This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the uniqu…
Recent advances in Reinforcement Learning (RL) have demonstrated promising results in autonomous car racing. However, two fundamental challenges remain: sparse rewards, which hinder efficient learning process, and the quality of demonstrations, which directly affects the effectiveness of RL from Dem
Shape abstraction, simplifying shape representation into a set of primitives, is a fundamental topic in computer vision. The choice of primitives shapes the structure of world understanding, yet achieving both high abstraction accuracy and versatility remains challenging. In this paper, we introduce…
Collision detection is a fundamental problem across various fields such as robotics, physical simulation, and computer graphics. While numerous studies have provided efficient solutions, based on the well-known Gilbert, Johnson, and Keerthi (GJK) algorithm and Expanding Polytope Algorithm (EPA), exi…
The gradient of typical differentiable simulation is uninformative for two reasons: 1) non-smoothness in contact dynamics not considered properly, and 2) excessive local minima generated from the smoothing procedure. To tackle this issue, we first propose differentiable contact dynamics with an inva…
In this paper, we present a new multibody physics simulation framework that utilizes the subsystem-based struc-ture and the Alternating Direction Method of Multiplier (ADMM). The major challenge in simulating complex high degree of freedom systems is a large number of coupled con-straints and large-…
While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR met…
For many robotic manipulation and contact tasks, it is crucial to accurately estimate uncertain object poses, for which certain geometry and sensor information are fused in some optimal fashion. Previous results for this problem primarily adopt sampling-based or end-to-end learning methods, which ye…
"Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high temporal resolution, several attempts have explored the potential of events for guiding deblurring. These methods genera…
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images. Having long been proven to be effective in image restoration tasks, a single lane of encoder-decoder architecture overlooks the characteristic of deblurring, where a blurry…
We propose a novel real-time physically-accurate simulator for long flexible cable manipulation. We first discretize the cable into multiple rigid link segments, each with complementarity-based contact model and inter-segment compliant coupling; and partition the cable into a number of subsystems, e…
We propose a novel real-time physically-accurate simulation framework for the snap connection process. For this, we first notice the peculiarities of the process, namely, small/smooth deformation, stiff connector and segmented contact. We then design our simulation to fully exploit these peculiariti…