623 citations · 795 across the 19 of their papers we have counts for
19 papers
Simulation-aided Learning from Demonstration for Robotic LEGO Construction
Ruixuan Liu, Alan Chen, Xusheng Luo +1
Recent advancements in manufacturing have a growing demand for fast, automatic prototyping (i.e. assembly and disassembly) capabilities to meet users' needs. This paper studies aut…
A Lightweight and Transferable Design for Robust LEGO Manipulation
Ruixuan Liu, Yifan Sun, Changliu Liu
Lego is a well-known platform for prototyping pixelized objects. However, robotic Lego prototyping (i.e., manipulating Lego bricks) is challenging due to the tight connections and…
Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Xusheng Luo, Shaojun Xu, Ruixuan Liu +1
Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on a single formula for individual or groups of robot…
Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Ruixuan Liu, Rui Chen, Abulikemu Abuduweili +1
Human-robot collaboration (HRC) is one key component to achieving flexible manufacturing to meet the different needs of customers. However, it is difficult to build intelligent rob…
Robotic LEGO Assembly and Disassembly from Human Demonstration
Ruixuan Liu, Yifan Sun, Changliu Liu
This paper studies automatic prototyping using LEGO. To satisfy individual needs and self-sustainability, this paper presents a framework that learns the assembly and disassembly s…
GUARD: A Safe Reinforcement Learning Benchmark
Weiye Zhao, Yifan Sun, Feihan Li +4
Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interactio…
Zero-shot Transferable and Persistently Feasible Safe Control for High Dimensional Systems by Consistent Abstraction
Tianhao Wei, Shucheng Kang, Ruixuan Liu +1
Safety is critical in robotic tasks. Energy function based methods have been introduced to address the problem. To ensure safety in the presence of control limits, we need to desig…
Task-Agnostic Adaptation for Safe Human-Robot Handover
Ruixuan Liu, Rui Chen, Changliu Liu
Human-robot interaction (HRI) is an important component to improve the flexibility of modern production lines. However, in real-world applications, the task (\ie the conditions tha…
Quantized anomalous Hall resistivity achieved in molecular beam epitaxy-grown MnBi2Te4 thin films
Yunhe Bai, Yuanzhao Li, Jianli Luan +17
The intrinsic magnetic topological insulator MnBi2Te4 provides a feasible pathway to high temperature quantum anomalous Hall (QAH) effect as well as various novel topological quant…
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Ruixuan Liu, Yanlin Wang, Yang Cao +4
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the prob…
Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm
Ruixuan Liu, Rui Chen, Changliu Liu
The need to increase the flexibility of production lines is calling for robots to collaborate with human workers. However, existing interactive industrial robots only guarantee int…
FLDP: Flexible strategy for local differential privacy
Dan Zhao, Suyun Zhao, Ruixuan Liu +3
Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is…
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu +4
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model…
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
Jingwei Yi, Fangzhao Wu, Chuhan Wu +3
News recommendation is critical for personalized news access. Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, w…
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Lingjuan Lyu +2
Federated learning is widely used to learn intelligent models from decentralized data. In federated learning, clients need to communicate their local model updates in each iteratio…
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
Ruixuan Liu, Changliu Liu
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC). However, it is challenging to build data-driven models for human intentio…
Moire Superlattice Modulations in Single-Unit-Cell FeTe Films Grown on NbSe2 Single Crystals
Han-Bin Deng, Yuan Li, Zili Feng +15
Interface can be a fertile ground for exotic quantum states, including topological superconductivity, Majorana mode, fractal quantum Hall effect, unconventional superconductivity,…
FLAME: Differentially Private Federated Learning in the Shuffle Model
Ruixuan Liu, Yang Cao, Hong Chen +2
Federated Learning (FL) is a promising machine learning paradigm that enables the analyzer to train a model without collecting users' raw data. To ensure users' privacy, differenti…
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa +1
As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has…