Author name cluster

Renming Liu

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.

3 papers

2 author rows

ICRA Conference 2025 Conference Paper

RM-Planner: Integrating Reinforcement Learning with Whole-Body Model Predictive Control for Mobile Manipulation

Zixuan Zhuang
Le Zheng
Wanyue Li
Renming Liu
Peng Lu
Hui Cheng

Mobile manipulation is a crucial problem in various real-world applications. However, existing methods have demonstrated unsatisfactory training efficiency and sparse rewards, requiring complex coordination strategies between the mobile base and arm. In this paper, we propose RM-Planner, a planning method for mobile manipulation tasks in unknown complex environments. By adopting a two-layer hierarchical framework, we utilize a whole-body Model Predictive Control (MPC)-based low-level planner to track subgoals and generate aggressive but safe joint commands throughout the entire manipulation process, while a Reinforcement Learning (RL)based high-level policy directly uses 3D point cloud representations of the environment, guiding the robot to achieve optimal manipulation postures based on current observations and specific task objectives. We conduct extensive simulations and real-world experiments, where RM-planner significantly outperforms state-of-the-art methods. Our code will be released at https://github.com/SYSU-RoboticsLab/RM-Planner.git.

Details

TIST Journal 2024 Journal Article

Deep Learning in Single-cell Analysis

Dylan Molho
Jiayuan Ding
Wenzhuo Tang
Zhaoheng Li
Hongzhi Wen
Yixin Wang
Julian Venegas
Wei Jin

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

Details DOI

ICML Conference 2024 Conference Paper

Graph Positional and Structural Encoder

Semih Cantürk
Renming Liu
Olivier Lapointe-Gagné
Vincent Létourneau
Guy Wolf
Dominique Beaini
Ladislav Rampásek

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all graph prediction tasks is a challenging and unsolved problem. Here, we present the Graph Positional and Structural Encoder (GPSE), the first-ever graph encoder designed to capture rich PSE representations for augmenting any GNN. GPSE learns an efficient common latent representation for multiple PSEs, and is highly transferable: The encoder trained on a particular graph dataset can be used effectively on datasets drawn from markedly different distributions and modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly outperform those that employ explicitly computed PSEs, and at least match their performance in others. Our results pave the way for the development of foundational pre-trained graph encoders for extracting positional and structural information, and highlight their potential as a more powerful and efficient alternative to explicitly computed PSEs and existing self-supervised pre-training approaches. Our framework and pre-trained models are publicly available at https: //github. com/G-Taxonomy-Workgroup/GPSE. For convenience, GPSE has also been integrated into the PyG library to facilitate downstream applications.

Details