Author name cluster

Fangyi Zhang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

ICRA Conference 2024 Conference Paper

Learning Fabric Manipulation in the Real World with Human Videos

Robert Lee
Jad Abou-Chakra
Fangyi Zhang
Peter Corke

Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods however rely heavily on simulation, which is still limited by the large sim-to-real gap of deformable objects or rely on large datasets. A promising alternative is to learn fabric manipulation directly from watching humans perform the task. In this work, we explore how demonstrations for fabric manipulation tasks can be collected directly by humans, providing an extremely natural and fast data collection pipeline. Then, using only a handful of such demonstrations, we show how a pick-and-place policy can be learned and deployed on a real robot, without any robot data collection at all. We demonstrate our approach on a fabric smoothing and folding task, showing that our policy can reliably reach folded states from crumpled initial configurations. Code, video and data are available on the project website: https://sites.google.com/view/foldingbyhand

Details

IROS Conference 2023 Conference Paper

Re-Evaluating Parallel Finger-Tip Tactile Sensing for Inferring Object Adjectives: An Empirical Study

Fangyi Zhang
Peter Corke

Finger-tip tactile sensors are increasingly used for robotic sensing to establish stable grasps and to infer object properties. Promising performance has been shown in a number of works for inferring adjectives that describe the object, but there remains a question about how each taxel contributes to the performance. This paper explores this question with empirical experiments, leading insights for future finger-tip tactile sensor usage and design: one tactile sensor instead of a pair of sensors is sufficient for symmetric objects and interaction motions; dense taxels are beneficial for texture-related adjectives, but can be distracting to non-texture-related ones; and a frame-rate much lower than the BioTac sensor can satisfy the demand of inferring object adjectives in the PHAC-2 dataset.

Details

ICLR Conference 2022 Conference Paper

Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space

Yaohua Wang
Yaobin Zhang
Fangyi Zhang
Senzhang Wang
Ming Lin 0002
YuQi Zhang
Xiuyu Sun

Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to $k$NN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS.

Details

NeurIPS Conference 2022 Conference Paper

Robust Graph Structure Learning via Multiple Statistical Tests

Yaohua Wang
Fangyi Zhang
Ming Lin
Senzhang Wang
Xiuyu Sun
Rong Jin

Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges. It is well known that pairwise similarities between images are sensitive to the noise in feature representations, leading to unreliable graph structures. We address this problem from the viewpoint of statistical tests. By viewing the feature vector of each node as an independent sample, the decision of whether creating an edge between two nodes based on their similarity in feature representation can be thought as a ${\it single}$ statistical test. To improve the robustness in the decision of creating an edge, multiple samples are drawn and integrated by ${\it multiple}$ statistical tests to generate a more reliable similarity measure, consequentially more reliable graph structure. The corresponding elegant matrix form named $\mathcal{B}$$\textbf{-Attention}$ is designed for efficiency. The effectiveness of multiple tests for graph structure learning is verified both theoretically and empirically on multiple clustering and ReID benchmark datasets. Source codes are available at https: //github. com/Thomas-wyh/B-Attention.

PDF Details

ICRA Conference 2017 Conference Paper

The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Jürgen Leitner
Adam W. Tow
Niko Sünderhauf
Jake E. Dean
Joseph W. Durham
Matthew Cooper 0005
Markus Eich
Chris Lehnert

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems - including perception and manipulation - instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.

Details

ICRA Conference 2015 Conference Paper

Asynchronous blind signal decomposition using tiny-length code for Visible Light Communication-based indoor localization

Fangyi Zhang
Kejie Qiu
Ming Liu 0001

Indoor localization is a fundamental capability for service robots and indoor applications on mobile devices. To realize that, the cost and performance are of great concern. In this paper, we introduce a lightweight signal encoding and decomposition method for a low-cost and low-power Visible Light Communication (VLC)-based indoor localization system. Firstly, a Gold-sequence-based tiny-length code selection method is introduced for light encoding. Then a correlation-based asynchronous blind light-signal decomposition method is developed for the decomposition of the lights mixed with modulated light sources. It is able to decompose the mixed light-signal package in real-time. The average decomposition time-cost for each frame is 20 ms. By using the decomposition results, the localization system achieves accuracy at 0. 56 m. These features outperform other existing low-cost indoor localization approaches, such as WiFiSLAM.

Details

IROS Conference 2015 Conference Paper

Visible Light Communication-based indoor localization using Gaussian Process

Kejie Qiu
Fangyi Zhang
Ming Liu 0001

For mobile robots and position-based services, such as healthcare service, precise localization is the most fundamental capability while low-cost localization solutions are with increasing need and potentially have a wide market. A low-cost localization solution based on a novel Visible Light Communication (VLC) system for indoor environments is proposed in this paper. A number of modulated LED lights are used as beacons to aid indoor localization additional to illumination. A Gaussian Process(GP) is used to model the intensity distributions of the light sources. A Bayesian localization framework is constructed using the results of the GP, leading to precise localization. Path-planning is hereby feasible by only using the GP variance field, rather than using a metric map. Dijkstra's algorithm-based path-planner is adopted to cope with the practical situations. We demonstrate our localization system by real-time experiments performed on a tablet PC in an indoor environment.

Details