31 citations · 92 across the 16 of their papers we have counts for
16 papers
Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data
Jialiang Li, Yi Qiao, Yunhan Guo +2
Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. How…
IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
Yifan Li, Lichi Li, Anh Dao +15
While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks la…
HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors
Shutong Zhang, Yi-Ling Qiao, Guanglei Zhu +6
Various heuristic objectives for modeling hand-object interaction have been proposed in past work. However, due to the lack of a cohesive framework, these objectives often possess…
Dynamic Mesh-Aware Radiance Fields
Yi-Ling Qiao, Alexander Gao, Yiran Xu +3
Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent…
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
Zhou Xian, Theophile Gervet, Zhenjia Xu +3
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitemen…
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
Xuan Li, Yi-Ling Qiao, Peter Yichen Chen +4
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a v…
Differentiable Analog Quantum Computing for Optimization and Control
Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao +2
We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quan…
NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos
Yi-Ling Qiao, Alexander Gao, Ming C. Lin
We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. To decouple the learning of underlying scene geometry…
Differentiable Hybrid Traffic Simulation
Sanghyun Son, Yi-Ling Qiao, Jason Sewall +1
We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated…
Differentiable Simulation of Soft Multi-body Systems
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1
We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We…
Efficient Differentiable Simulation of Articulated Bodies
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1
We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks, and gradient…
Scalable Differentiable Physics for Learning and Control
Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilitie…
OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors
Jing Liang, Yi-Ling Qiao, Tianrui Guan +1
We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our s…
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry
Yi-Ling Qiao, Lin Gao, Shu-Zhi Liu +3
Reflectional symmetry is ubiquitous in nature. While extrinsic reflectional symmetry can be easily parametrized and detected, intrinsic symmetry is much harder due to the high solu…
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
Yi-Ling Qiao, Lin Gao, Jie Yang +3
3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is…
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences
Yi-Ling Qiao, Lin Gao, Yu-Kun Lai +1
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networ…