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Yong Guan

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14 papers

2 author rows

AAAI Conference 2024 Conference Paper

Knowledge-Aware Neuron Interpretation for Scene Classification

Yong Guan
Freddy Lécué
Jiaoyan Chen
Ru Li
Jeff Z. Pan

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and more attentions. However, current methods rarely incorporate external knowledge and still suffer from three limitations: (1) Neglecting concept completeness. Merely selecting concepts may not sufficient for prediction. (2) Lacking concept fusion. Failure to merge semantically-equivalent concepts. (3) Difficult in manipulating model behavior. Lack of verification for explanation on original model. To address these issues, we propose a novel knowledge-aware neuron interpretation framework to explain model predictions for image scene classification. Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts. Our method, incorporating complete concepts, effectively provides better prediction explanations compared to baselines. Furthermore, for concept fusion, we introduce a knowledge graph-based method known as Concept Filtering, which produces over 23% point gain on neuron behaviors for neuron interpretation. At last, we propose Model Manipulation, which aims to study whether the core concepts based on ConceptNet could be employed to manipulate model behavior. The results show that core concepts can effectively improve the performance of original model by over 26%.

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ICLR Conference 2024 Conference Paper

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

Jifan Yu
Xiaozhi Wang
Shangqing Tu
Shulin Cao
Daniel Zhang-Li
Xin Lv
Hao Peng 0015
Zijun Yao 0002

The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For ability modeling, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering 19 tasks. (2) For data, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For evaluation criteria, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models, and a unique self-contrast metric for automatically evaluating knowledge-creating ability. We evaluate 21 open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset will be updated every three months to provide timely references for developing LLMs and knowledge-related systems.

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IROS Conference 2021 Conference Paper

Fast and Unsupervised Non-Local Feature Learning for Direct Volume Rendering of 3D Medical Images

Xinmei Fu
Zhenzhou Shao
Ying Qu 0001
Yong Guan
Yibo Zou
Zhiping Shi 0002
Jindong Tan

To improve the efficiency of medical visualization for computer aided surgery, we propose a fast and unsupervised 3D-CNN based non-local feature learning network. The proposed network consists of an encoder structure and a decoder structure. The encoder of the network projects the cube into a high-dimensional feature space, and the decoder of the network reconstructs the cube from the feature space. The decoder of the network serves as a dictionary shared by the cube to enforce the features for similar parts to be similar although they may distribute at disjointed locations. With such structures, the network is able to extract non-local features of the entire data. Moreover, a sparse constraint is incorporated into the network to increase the discriminative of the non-local features. Then the extracted non-local features of each voxel are fused with the corresponding position matrix and Hessian matrix for the voxel classification using Random Forest. Finally, a multidimensional transfer function is designed to enable the volume rendering. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods with much less training time.

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IROS Conference 2020 Conference Paper

Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

Zhenzhou Shao
Ying Qu 0001
Guangli Ren
Guohui Wang
Yong Guan
Zhiping Shi 0002
Jindong Tan

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95. 51% for robotic grasping detection.

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IROS Conference 2019 Conference Paper

Inverse Dynamics Modeling of Robotic Manipulator with Hierarchical Recurrent Network

Pengfei Sun
Zhenzhou Shao
Ying Qu 0001
Yong Guan
Jindong Tan

Inverse dynamics modeling is a critical problem for the computed-torque control of robotic manipulator. This paper presents a novel recurrent network based on the modified Simple Recurrent Unit (SRU) with hierarchical memory (SRU-HM), which is achieved by the nested SRU structure. In this way, it enables the capability to retain the long-term information in the distant past, compared with the conventional stacked structure. The hidden state of SRU is able to provide more complete information relevant to current prediction. Experimental results demonstrate that the proposed method can improve the accuracy of dynamics model greatly, and outperforms the state-of-the-art methods.

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ICRA Conference 2018 Conference Paper

Design and Test of an In-Vivo Robotic Camera Integrated with Optimized Illumination System for Single-port Laparoscopic Surgery

Xiaolong Liu 0002
A. Reza Yazdanpanah
Tao Zuo
Yong Guan
Gregory J. Mancini
Jindong Tan

This paper proposes a novel in-vivo robotic laparo-scopic camera design with an optimized illumination system, which is a crucial component for achieving high imaging quality. The robotic camera design with three extendable wings can reserve sufficient on-board space to harbor the optimized illumination system without affecting the compactness of the camera. We contribute a freeform optical lens design method and develop three miniature optical lenses for the LEDs to achieve greater than 95% illumination uniformity, greater than 14, 000 lx illuminance on a target plane with a distance of 100 mm, and greater than 89% optical efficiency. The prototype is implemented and experimentally tested, which demonstrates great performance of the in-vivo robotic laparoscopic camera and the significance of the optimized illumination system.

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IROS Conference 2018 Conference Paper

Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations

Zhenzhou Shao
Hongfa Zhao
Jiexin Xie
Ying Qu 0001
Yong Guan
Jindong Tan

To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue. Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way. To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data. On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements. Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.

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IROS Conference 2017 Conference Paper

A fast search algorithm based on image pyramid for robotic grasping

Guangli Ren
Zhenzhou Shao
Yong Guan
Ying Qu 0001
Jindong Tan
Hongxing Wei
Guofeng Tong

To improve the search efficiency of robotic grasping detection, this paper presents a novel search algorithm based on the image pyramid. It significantly reduces the search space for grasping position detection using the coarse-to-fine strategy. The proposed method searches the positions from the top layer of the pyramid, and initializes the search area at the next layer. The sparse automatic encoder is employed to construct the model which is used to evaluate the grasp quality. The experimental results demonstrate that the proposed search algorithm can improve efficiency of the robotic grasping detection with the comparative performance on the grasp quality.

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IROS Conference 2017 Conference Paper

Formalization and analysis of jacobian matrix in screw theory and its application in kinematic singularity

Aixuan Wu
Zhiping Shi 0002
Xiumei Yang
Yong Guan
Yongdong Li
Xiaoyu Song

Accurate specification and rigorous analysis of Jacobian matrix are indispensable to guarantee correct evaluation on the manipulator kinematics performance. In this paper, a formal analysis method of the Jacobian matrix in the screw theory is presented by using the higher-order logic theorem prover HOL4. Formalizations of twists and the forward kinematics are characterized with the product of exponential formula and the theory of functional matrices. To the best of our knowledge, this work is the first to formally reason about the spatial Jacobian using theorem proving. The formal modeling and analysis of a 3-DOF planar manipulator substantiate the effectiveness and applicability of the proposed approach to formally verify the kinematics properties of manipulator.

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ICRA Conference 2014 Conference Paper

A framework of model checking guided test vector generation for the 6DOF manipulator

Yilin Lu
Yong Guan
Xiaojuan Li
Rui Wang 0024
Jie Zhang 0074

In designing robot control systems, simulation is still the primary approach to verifying the functions of circuit descriptions written in hardware design language. The validity of the verification depends on the coverage metric. But not all state spaces can be specified in a simulation. Model checking can overcome the shortcomings of simulation, because all of the state spaces can be traversed. Robot control system is an important part of the robot, used in the control of the manipulator to perform specific tasks. Therefore, in this paper a verification framework is presented for design correctness of robotic manipulator, which combines simulation and model checking. The framework is also applied to other control system or control hardware. Model checking guides the generating of test vectors, and makes the functional coverage reach 100% quickly. In this paper, a manipulator designed with six degrees of freedom is verified. The results of the verification of the manipulator show that the verification framework is effective for checking the design correctness of robotic manipulator.

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ICRA Conference 2014 Conference Paper

Formal verification of a collision-free algorithm of dual-arm robot in HOL4

Liming Li
Zhiping Shi 0002
Yong Guan
Chunna Zhao
Jie Zhang 0074
Hongxing Wei

Possessing two manipulators heightens the ability of dual-arm robots (DAR) to conduct complex tasks, while raising hazard that the two manipulators might collide with each other or with other objects. DARs are usually equipped with a collision-free motion planning algorithms (CFMPA) to prevent the two manipulators from colliding. The CFMPA searches the motion paths of robot manipulators, which are expected to be as short and smooth as possible under the premise of ensuring safety. It is important to ensure that the algorithm is correct and efficient. It is not enough to apply traditional test methods to determine whether DARs can work in safety-critical applications. In this paper, theorem proving technology is employed to analyze the correctness and efficiency of a classical CFMPA. The CFMPA is outlined, and then formalized in high order logic with the theorem prover HOL4. An inconsistency in the range of motions of the robot manipulators in the algorithm is discovered. An improved algorithm is therefore proposed. Formal verification with HOL4 proves the correctness and efficiency of the proposed algorithm that has already run on a real DAR as well, in conformity with our expectation.

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ICRA Conference 2014 Conference Paper

Geometry constrained sparse embedding for multi-dimensional transfer function design in direct volume rendering

Zhenzhou Shao
Yong Guan
Hongsheng He
Jindong Tan

Direct volume rendering (DVR) is commonly employed for the medical visualization. Multi-dimensional transfer functions are used in DVR to emphasize the region of interest in details. However, it is impractical to interact directly with the functions in more than three dimension. This paper proposes a novel framework called geometry constrained sparse embedding (GCSE) for dimensionality reduction (DR). GCSE allows the conventional DR methods to be applied to a dictionary with much smaller atoms instead. The mapping derived from the dictionary feeds to the original features to obtain the ones in the reduced dimension. To obtain a good dictionary, the intrinsic structure of features is encoded in the sparse embedding based on a geometry distance. In addition, stochastic gradient descent algorithm is employed to speed up the dictionary learning. Various experiments have been conducted using both synthetic and real CT data sets. Compared with conventional methods, GCSE not only produces the comparable results, but also performs well with the capability to handle the large data set more powerfully. The rendering results using the real CT data has demonstrated the effectiveness of GCSE.

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ICRA Conference 2014 Conference Paper

RGMP-ROS: A real-time ROS architecture of hybrid RTOS and GPOS on multi-core processor

Hongxing Wei
Zhen Huang
Qiang Yu
Miao Liu
Yong Guan
Jindong Tan

Recently, the open-source robot operating system (ROS) has been growing rapidly in the robotics community. However, the ROS runs on Linux, which does not provide timing guarantees for robot motion. This paper present a hybrid real-time ROS architecture on multi-core processor “RGMP-ROS”, which consists of two parts including the non-real-time subsystem “GPOS (General Operating system)” and the real-time one “RTOS (Real-time Operating system)”. The GPOS is comprised of non-real-time ROS nodes running in Linux, while the RTOS only contains real-time ROS nodes running in Nuttx. To get higher operational efficiency, the RGMP-ROS system is executed by a dual-core processor, one CPU for GPOS and the other for RTOS. The RGMP-ROS has used in the controller of a 6-DOF modular manipulator, and its effectiveness and efficiency are demonstrated by software testing and experiments. The main contributions of the present work lie in the realization of real-time ROS architecture and the application of multi-core processor in the hybrid control of an industrial robot.

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ICRA Conference 2014 Conference Paper

Timed automata based motion planning for a self-assembly robot system

Rui Wang 0024
Ping Luo
Yong Guan
Hongxing Wei
Xiaojuan Li
Jie Zhang 0074
Xiaoyu Song

Sambot is a module robot system, with the advantages of self-assembly. A target robotic configuration can be organized by a group of Sambots. A novel motion planning method for Sambot configuration using model checking is presented in this paper. This hierarchical method contains two layers. The abstract logic layer is responsible for the discrete planning of Sambots configuration. The robot and the environment are all modeled as timed automata. System requirements are formalized as Computational Tree Logic (CTL) formulas. Model checking is applied on the system model. The verification result gives the optimal discrete plans for the configuration of Sambot. In physical layer, a sample-based planner generates the trajectory trace considering the dynamics of Sambot and the suggested high level plans. The experiment results illustrate the effectiveness of our approach.

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