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IEEE Systems Journal 2025年第1期19卷 317-326页

作者： Liu, Weiwei Hu, Wenxuan Jing, Wei Lei, Lanxin Gao, Lingping Liu, Yong Zhejiang University The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Zhejiang University Zhejiang 310027 China Alibaba DAMO Academy Autonomous Driving Lab Zhejiang 311121 China Huzhou University College of Information Engineering Zhejiang 313000 China

Autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods. © 2007-2012 IEEE.

关键词： Reinforcement learning

FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction

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arXiv 2025年

作者： Rotondi, Dennis Scaparro, Fabio Blum, Hermann Arras, Kai O. Socially Intelligent Robotics Lab Institute for Artificial Intelligence University of Stuttgart Germany Robot Perception and Learning Lab LAMARR Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contrast, our goal is to develop a representation that enables robots to directly interact with their environment by identifying both the location of functional interactive elements and how these can be used. To achieve this, we focus on detecting and storing objects at a finer resolution, focusing on affordance-relevant parts. The primary challenge lies in the scarcity of data that extends beyond instance-level detection and the inherent difficulty of capturing detailed object features using robotic sensors. We leverage currently available 3D resources to generate 2D data and train a detector, which is then used to augment the standard 3D scene graph generation pipeline. Through our experiments, we demonstrate that our approach achieves functional element segmentation comparable to state-of-the-art 3D models and that our augmentation enables task-driven affordance grounding with higher accuracy than the current solutions. © 2025, CC BY-NC-ND.

关键词： Semantics

TransVOS: Video object segmentation with transformers

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arXiv 2021年

作者： Mei, Jianbiao Wang, Mengmeng Lin, Yeneng Yuan, Yi Liu, Yong Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Engineering Zhejiang University NetEase Fuxi AI Lab

Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this task is how to model the dependency both among different frames and inside every frame. However, most of these methods neglect the spatial relationships (inside each frame) and do not make full use of the temporal relationships (among different frames). In this paper, we propose a new transformer-based framework, termed TransVOS, introducing a vision transformer to fully exploit and model both the temporal and spatial relationships. Moreover, most STM-based approaches employ two separate encoders to extract features of two significant inputs, i.e., reference sets (history frames with predicted masks) and query frame (current frame), respectively, increasing the models' parameters and complexity. To slim the popular two-encoder pipeline while keeping the effectiveness, we design a single two-path feature extractor to encode the above two inputs in a unified way. Extensive experiments demonstrate the superiority of our TransVOS over state-of-the-art methods on both DAVIS and YouTube-VOS datasets. Codes are available at https://***/sallymmx/***. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement learning

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arXiv 2024年

作者： Weiwei, Liu Wenxuan, Hu Wei, Jing Lanxin, Lei Lingping, Gao Yong, Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou310027 China The Advanced Perception on Robotics and Intelligent Learning Lab Huzhou Institute Zhejiang University Huzhou China College of Information Engineering Huzhou University Huzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

Autonomous vehicles trained through Multi-Agent Reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the Personality Modeling Network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization compared to traditional MARL methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

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Biomedical Engineering Frontiers 2022年第1期3卷 298-308页

作者： Heqin Zhu Qingsong Yao Li Xiao S.Kevin Zhou Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China Center for Medical Imaging RoboticsAnalytic Computing&Learning(MIRACLE)School of Biomedical Engineering&Suzhou Institute for Advanced ResearchUniversity of Science and Technology of ChinaSuzhou 215123China

Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark *** accurate and automatic localization of anatomical landmarks plays an essential role in medical image ***,recent deep learning-based methods only utilize limited data from a single *** is promising and desirable to build a model learned from different regions which harnesses the power of big *** model consists of a local network and a global network,which capture local features and global features,*** local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global *** evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical *** experimental results show that our model improves the detection accuracy compared to the state-of-the-art *** model makes the first attempt to train a single network on multiple datasets for landmark *** results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

关键词： convolution utilize separable

Raising Body Ownership in End-to-End Visuomotor Policy learning via Robot-Centric Pooling

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Raising Body Ownership in End-to-End Visuomotor Policy Learn...

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Zheyu Zhuang Ville Kyrki Danica Kragic Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Automation (EEA) Intelligent Robotics Group Aalto University Espoo Finland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomo-tor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot’s proprioceptive states, thereby extracting robot-centric image representations for policy learning. Leveraging contrastive learning techniques, RcP integrates seamlessly with existing visuomotor policy learning frameworks and is trained jointly with the policy using the same dataset, requiring no extra data collection involving self-distractors. We evaluate the proposed method with reaching tasks in both simulated and real-world settings. The results demonstrate that RcP significantly enhances the policies’ robustness against various unseen distractors, including self-distractors, positioned at different locations. Additionally, the inherent robot-centric characteristic of RcP enables the learnt policy to be far more resilient to aggressive pixel shifts compared to the baselines. Code available at: https://***/Zheyu-Zhuang/RcP

关键词： Propioception Performance gain Image representation Data collection Feature extraction Robustness History Robots intelligent robots Resilience

An efficient minimum vocabulary construction algorithm for language modeling 12

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25th International Conference on Industrial Engineering and Other Applications of Applied intelligent Systems, IEA/AIE 2012

作者： Lin, Sina Qin, Zengchang Huang, Zehua Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China School of Advanced Engineering Beihang University China Robotics Institute Carnegie Mellon University Pittsburgh United States School of Medicine Boston University Boston United States

ISBN: (纸本)9783642310867

In learning a new word by a dictionary, we first need to know a set of "basic words" which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification. © 2012 Springer-Verlag.

关键词： Classification (of information)

Dexterous manipulation graphs

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arXiv 2018年

作者： Cruciani, Silvia Smith, Christian Kragic, Danica Hang, Kaiyu Robotics Perception and Learning Lab EECS KTH Royal Institute of Technology Stockholm Sweden Robotics Institute Institute for Advanced Study Hong Kong University of Science and Technology Hong Kong

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot programming

Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

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arXiv 2018年

作者： Lundell, Jens Krug, Robert Schaffernicht, Erik Stoyanov, Todor Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland AASS Research Center Örebro University Sweden Robotics Learning and Perception lab KTH Royal Institute of Technology Sweden

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

learning Intra-group Cooperation in Multi-agent Systems

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Learning Intra-group Cooperation in Multi-agent Systems

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Weiwei Liu Shanqi Liu Jian Yang Yong Liu The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Enginneering Zhejiang University Hangzhou China China Research and Development Academy of Machinery Equipment Beijing China Huzhou Institute of Zhejiang University Huzhou China

ISBN: (纸本)9781665431545

Reinforcement learning is one of the algorithms used in multi-agent systems to promote agent cooperation. However, most current multi-agent reinforcement learning algorithms improve the communication capabilities of agents for cooperation, but the overall communication is costly and even harmful due to bandwidth limitations. In addition, de-centralized execution cannot generate joint actions, which is not conducive to cooperation. Therefore, we proposed the Hierarchical Group Cooperation Network (HGCN). advanced strategy, Group Network (GroNet), learns to group all agents based on their state rather than their location. The Low-level strategy, Group Cooperation Network (GCoNet), is a method of centralized training and centralized execution within a group, which effectively promotes agent collaboration. Finally, we validated our method in various experiments.

关键词： Training Mechatronics Machine vision Collaboration Reinforcement learning Bandwidth Task analysis