arXiv subjects

Chenrui Zhang

Publications and source records attributed to Chenrui Zhang.

16 recordsLinked to original sources

arXiv2026

Unified Structural-Hydrodynamic Modeling of Underwater Underactuated Mechanisms and Soft Robots

Underwater robots are widely deployed for ocean exploration and manipulation. Underactuated mechanisms are advantageous in aquatic environments because reducing actuator count lowers motor-leakage risk while introducing inherent mechanical compliance. However, accurate modeling of underwater underactuated and soft robotic systems remains challenging, as it requires identifying high-dimensional structural and hydrodynamic parameters. In this work, we propose a trajectory-driven global optimization framework for unified structural-hydrodynamic modeling of underwater multibody systems. Inspired by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed approach simultaneously identifies coupled elastic, damping, and distributed hydrodynamic parameters through trajectory-level matching between simulated and experimental motion. This enables high-fidelity reproduction of underactuated mechanisms and compliant soft robotic systems in underwater environments, using as little as a single video recording. We first validate the framework on a link-by-link underactuated multibody mechanism, demonstrating accurate identification of distributed hydrodynamic coefficients, with normalized end-effector position error below 5% across multiple trajectories, initial conditions, and both active-passive and fully passive configurations. The modeling strategy is further validated on an asymmetric octopus-inspired soft arm, confirming its effectiveness for compliant soft robotic systems. Finally, eight identified arms are assembled into a swimming octopus robot, where the unified parameter set enables realistic whole-body behavior without additional retuning. These results demonstrate the scalability and transferability of the proposed structural-hydrodynamic modeling framework across underwater underactuated and soft robotic systems.

cs.RO↗

arXiv2026

Octopus-like Reaching Motion: A Perspective Inspired by Whipping

The stereotypical reaching motion of the octopus arm has drawn growing attention for its efficient control of a highly deformable body. Previous studies suggest that its characteristic bend propagation may share underlying principles with the dynamics of a whip. This work investigates whether whip-like passive dynamics in water can reproduce the kinematic features observed in biological reaching and their similarities and differences. Platform-based whipping tests were performed in water and air while systematically varying material stiffness and driving speed. Image-based quantification revealed that the Ecoflex Gel 2 arm driven at 150 rpm (motor speed) reproduced curvature propagation similar to that observed in octopus reaching. However, its bend-point velocity decreased monotonically rather than exhibiting the biological bell-shaped profile, confirming that the octopus reaching movement is not merely a passive whipping behavior. The absence of propagation in air further highlights the critical role of the surrounding medium in forming octopus-like reaching motion. This study provides a new perspective for understand biological reaching movement, and offers a potential platform for future hydrodynamic research.

cs.RO↗

arXiv2025

Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation

This technical report presents the champion solution of the Table Service Track in the ICRA 2025 What Bimanuals Can Do (WBCD) competition. We tackled a series of demanding tasks under strict requirements for speed, precision, and reliability: unfolding a tablecloth (deformable-object manipulation), placing a pizza into the container (pick-and-place), and opening and closing a food container with the lid. Our solution combines VR-based teleoperation and Learning from Demonstrations (LfD) to balance robustness and autonomy. Most subtasks were executed through high-fidelity remote teleoperation, while the pizza placement was handled by an ACT-based policy trained from 100 in-person teleoperated demonstrations with randomized initial configurations. By carefully integrating scoring rules, task characteristics, and current technical capabilities, our approach achieved both high efficiency and reliability, ultimately securing the first place in the competition.

cs.RO↗

arXiv2025

Robotic Sim-to-Real Transfer for Long-Horizon Pick-and-Place Tasks in the Robotic Sim2Real Competition

This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the ability to overcome typical sensing and actuation discrepancies during sim-to-real transfer and to achieve consistent performance without any algorithmic modifications. To accomplish this, a lightweight noise-resistant visual perception system and a nonlinearity-robust servo system are adopted. We conduct a series of tests in both simulated and real-world environments. The visual perception system achieves the speed of 11 ms per frame due to its lightweight nature, and the servo system achieves sub-centimeter accuracy with the proposed controller. Both exhibit high consistency during sim-to-real transfer. Benefiting from these, our robotic system took first place in the mineral searching task of the Robotic Sim2Real Challenge hosted at ICRA 2024.

cs.RO↗

arXiv2024

EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation

Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan, which serves multiple business domains via knowledge transfer and meets the diverse interests of users. However, existing methods typically follow an implicit modeling paradigm that blends the knowledge from both the source and target domains, and design intricate network structures to share learned embeddings or patterns between domains to improve recommendation accuracy. Since the transfer of interest signals is unsupervised, these implicit paradigms often struggle with the negative transfer resulting from differences in service functions and presentation forms across different domains. In this paper, we propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge. Specifically, we propose a novel label combination approach that enables the model to directly learn beneficial source domain interests through supervised learning, while excluding inappropriate interest signals. Moreover, we introduce a scene selector network to model the interest transfer intensity under fine-grained scenes. Offline experiments conducted on the industrial production dataset and online A/B tests validate the superiority and effectiveness of our proposed framework. Without complex network structures or training processes, EXIT can be easily deployed in the industrial recommendation system. EXIT has been successfully deployed in the online homepage recommendation system of Meituan App, serving the main traffic.

cs.IR↗

arXiv2023

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

As an effective tool for eliciting the power of Large Language Models (LLMs), prompting has recently demonstrated unprecedented abilities across a variety of complex tasks. To further improve the performance, prompt ensemble has attracted substantial interest for tackling the hallucination and instability of LLMs. However, existing methods usually adopt a two-stage paradigm, which requires a pre-prepared set of prompts with substantial manual effort, and is unable to perform directed optimization for different weak learners. In this paper, we propose a simple, universal, and automatic method named PREFER (Pompt Ensemble learning via Feedback-Reflect-Refine) to address the stated limitations. Specifically, given the fact that weak learners are supposed to focus on hard examples during boosting, PREFER builds a feedback mechanism for reflecting on the inadequacies of existing weak learners. Based on this, the LLM is required to automatically synthesize new prompts for iterative refinement. Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting. Extensive experiments demonstrate that our PREFER achieves state-of-the-art performance in multiple types of tasks by a significant margin. We have made our code publicly available.

cs.CL↗

arXiv2023

Enhancing Personalized Ranking With Differentiable Group AUC Optimization

AUC is a common metric for evaluating the performance of a classifier. However, most classifiers are trained with cross entropy, and it does not optimize the AUC metric directly, which leaves a gap between the training and evaluation stage. In this paper, we propose the PDAOM loss, a Personalized and Differentiable AUC Optimization method with Maximum violation, which can be directly applied when training a binary classifier and optimized with gradient-based methods. Specifically, we construct the pairwise exponential loss with difficult pair of positive and negative samples within sub-batches grouped by user ID, aiming to guide the classifier to pay attention to the relation between hard-distinguished pairs of opposite samples from the perspective of independent users. Compared to the origin form of pairwise exponential loss, the proposed PDAOM loss not only improves the AUC and GAUC metrics in the offline evaluation, but also reduces the computation complexity of the training objective. Furthermore, online evaluation of the PDAOM loss on the 'Guess What You Like' feed recommendation application in Meituan manifests 1.40% increase in click count and 0.65% increase in order count compared to the baseline model, which is a significant improvement in this well-developed online life service recommendation system.

cs.LG↗

arXiv2022

A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model

Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market liquidity and volatility is stronger. Although the inverse effect is relatively small, it is more pronounced with the state of the stock market. In all cases, the response is more pronounced in the short term than in the medium to long term, and the impact is asymmetric, with shocks stronger when the market is in a downward spiral.

q-fin.CP↗

arXiv2022

Deep learning based Chinese text sentiment mining and stock market correlation research

We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.

q-fin.CP↗

arXiv2022

Research on the correlation between text emotion mining and stock market based on deep learning

This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. Through the comparative study of the maximal information coefficient (MIC), it is found that the emotional characteristics obtained by applying the BERT model to the financial corpus can be reflected in the fluctuation of the stock market, which is conducive to effectively improve the prediction accuracy. At the same time, this paper combines in-depth learning with financial texts to further explore the impact mechanism of investor sentiment on the stock market through in-depth learning, which will help the national regulatory authorities and policy departments to formulate more reasonable policies and guidelines for maintaining the stability of the stock market.

q-fin.ST↗

arXiv2021

ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation is an important task in location-based social networks. It facilitates the relation modeling between users and locations. Recently, researchers recommend POIs by long- and short-term interests and achieve success. However, they fail to well capture the periodic interest. People tend to visit similar places at similar times or in similar areas. Existing models try to acquire such kind of periodicity by user's mobility status or time slot, which limits the performance of periodic interest. To this end, we propose to learn spatial-temporal periodic interest. Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest. In the short-term module, we construct various short-term sequences to acquire the spatial-temporal periodic interest of hourly, areal, and hourly-areal granularities, respectively. Finally, we apply inter-level attention to automatically integrate multiple interests. Experiments on two real-world datasets demonstrate the state-of-the-art performance of our method.

cs.IR↗

arXiv2021

Otaku: Intelligent Management System for Student-Intensive Dormitory

In most student dorms in developing countries, a large number of people live in single-function dorm units. The division of the dormitory is too fixed, resulting in the dormitory often lacking functional spaces such as entertainment, sports, meetings, etc. At the same time, a large number of people are likely to cause aggregation at a fixed time, which is not conducive to maintaining social distance under pandemic conditions such as COVID-19. This brings a lot of inconvenience to students' life studies and management staff. In this paper, we present a smart dormitory system named Otaku using the Internet of Things technology to integrate facilities related to student dormitory life. By splitting the dormitory into several different categories according to their functionality by using smart door lock design, the system can achieve a more effective and flexible resource allocation, which not only helps the school management but also benefits students.

cs.CY↗

arXiv2021

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue. However, most of the state-of-the-art auction mechanisms only focus on optimizing a single performance metric, e.g., either social welfare or revenue, and are not suitable for e-commerce advertising with various, dynamic, difficult to estimate, and even conflicting performance metrics. In this paper, we propose a new mechanism called Deep GSP auction, which leverages deep learning to design new rank score functions within the celebrated GSP auction framework. These new rank score functions are implemented via deep neural network models under the constraints of monotone allocation and smooth transition. The requirement of monotone allocation ensures Deep GSP auction nice game theoretical properties, while the requirement of smooth transition guarantees the advertiser utilities would not fluctuate too much when the auction mechanism switches among candidate mechanisms to achieve different optimization objectives. We deployed the proposed mechanisms in a leading e-commerce ad platform and conducted comprehensive experimental evaluations with both offline simulations and online A/B tests. The results demonstrated the effectiveness of the Deep GSP auction compared to the state-of-the-art auction mechanisms.

cs.GT↗

arXiv2019

TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning

Zero-shot and few-shot learning aim to improve generalization to unseen concepts, which are promising in many realistic scenarios. Due to the lack of data in unseen domain, relation modeling between seen and unseen domains is vital for knowledge transfer in these tasks. Most existing methods capture seen-unseen relation implicitly via semantic embedding or feature generation, resulting in inadequate use of relation and some issues remain (e.g. domain shift). To tackle these challenges, we propose a Transferable Graph Generation (TGG) approach, in which the relation is modeled and utilized explicitly via graph generation. Specifically, our proposed TGG contains two main components: (1) Graph generation for relation modeling. An attention-based aggregate network and a relation kernel are proposed, which generate instance-level graph based on a class-level prototype graph and visual features. Proximity information aggregating is guided by a multi-head graph attention mechanism, where seen and unseen features synthesized by GAN are revised as node embeddings. The relation kernel further generates edges with GCN and graph kernel method, to capture instance-level topological structure while tackling data imbalance and noise. (2) Relation propagation for relation utilization. A dual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs. The two propagations learn from each other in a dual learning fashion, which performs as an adaptation way for mitigating domain shift. All components are jointly optimized with a meta-learning strategy, and our TGG acts as an end-to-end framework unifying conventional zero-shot, generalized zero-shot and few-shot learning. Extensive experiments demonstrate that it consistently surpasses existing methods of the above three fields by a significant margin.

cs.LG↗

arXiv2018

Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification

Video representation learning is a vital problem for classification task. Recently, a promising unsupervised paradigm termed self-supervised learning has emerged, which explores inherent supervisory signals implied in massive data for feature learning via solving auxiliary tasks. However, existing methods in this regard suffer from two limitations when extended to video classification. First, they focus only on a single task, whereas ignoring complementarity among different task-specific features and thus resulting in suboptimal video representation. Second, high computational and memory cost hinders their application in real-world scenarios. In this paper, we propose a graph-based distillation framework to address these problems: (1) We propose logits graph and representation graph to transfer knowledge from multiple self-supervised tasks, where the former distills classifier-level knowledge by solving a multi-distribution joint matching problem, and the latter distills internal feature knowledge from pairwise ensembled representations with tackling the challenge of heterogeneity among different features; (2) The proposal that adopts a teacher-student framework can reduce the redundancy of knowledge learnt from teachers dramatically, leading to a lighter student model that solves classification task more efficiently. Experimental results on 3 video datasets validate that our proposal not only helps learn better video representation but also compress model for faster inference.

cs.CV↗

arXiv2018

Visual Data Synthesis via GAN for Zero-Shot Video Classification

Zero-Shot Learning (ZSL) in video classification is a promising research direction, which aims to tackle the challenge from explosive growth of video categories. Most existing methods exploit seen-to-unseen correlation via learning a projection between visual and semantic spaces. However, such projection-based paradigms cannot fully utilize the discriminative information implied in data distribution, and commonly suffer from the information degradation issue caused by "heterogeneity gap". In this paper, we propose a visual data synthesis framework via GAN to address these problems. Specifically, both semantic knowledge and visual distribution are leveraged to synthesize video feature of unseen categories, and ZSL can be turned into typical supervised problem with the synthetic features. First, we propose multi-level semantic inference to boost video feature synthesis, which captures the discriminative information implied in joint visual-semantic distribution via feature-level and label-level semantic inference. Second, we propose Matching-aware Mutual Information Correlation to overcome information degradation issue, which captures seen-to-unseen correlation in matched and mismatched visual-semantic pairs by mutual information, providing the zero-shot synthesis procedure with robust guidance signals. Experimental results on four video datasets demonstrate that our approach can improve the zero-shot video classification performance significantly.

cs.CV↗