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Stochastic-Aware Mamba Diffusion for Pedestrian Trajectory Prediction

Stochastic-Aware Mamba Diffusion for Pedestrian Trajectory P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ziyang Ren Ping Wei Haowen Tang Huan Li Jin Yang Jialu Qin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pedestrian trajectory prediction plays a crucial role in understanding human behavior and intentions. Due to the inherent randomness in human movement, current research constructs trajectories in stochastic space and uses diffusion models to reverse the denoising process. The commonly used denoising model, Transformer, is affected by uneven noise sampling, leading to confusion between temporal consistency and randomness across multiple time points. To address this issue, we propose a novel framework named Stochastic-Aware Mamba Diffusion (SAMD), which combines Stochastic-Aware Mamba with the Diffusion model to predict motion noise and motion states in the stochastic space. It utilizes a temporal aggregator to extract temporal consistency features. We construct a motion-selective state space model, which includes the adaptive transition between consistency motion states and stochastic states for balancing stability and diversity. The motion gating unit activates pertinent information within motion states to extract motion noise for trajectory prediction. Our approach achieves state-of-the-art results on the ETH-UCY and SDD datasets while significantly reducing the computational cost.

关键词： Pedestrians Computational modeling Noise reduction Noise Stochastic processes Predictive models Feature extraction Diffusion models Trajectory Data mining

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ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

ASPIRe: An Informative Trajectory Planner with Mutual Inform...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Kangjie Zhou Pengying Wu Yao Su Han Gao Ji Ma Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes an informative trajectory planning approach, namely, adaptive particle filter tree with sigma point-based mutual information reward approximation (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

关键词： Accuracy Target tracking Trajectory planning Robot sensing systems Real-time systems Particle filters Trajectory

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A Compact LiDAR Odometry and Mapping with Dynamic Removal

A Compact LiDAR Odometry and Mapping with Dynamic Removal

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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Zhang, Meifeng Jia, Yanpeng Shao, Shiliang Wang, Shiyi College of Artificial Intelligence Baoding University Baoding China The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350384185

Real-time state estimation and mapping is a fundamental function of unmanned systems. In this paper, we propose a compact 3D Lidar odometry and mapping method (C-LOAM) for real-time 6-DOF pose estimation and map construction for unmanned ground vehicles. Our approach firstly remove dynamic objects from the environment by comparing differences between two consecutive range images. We then extract ground points from the range image. The remaining points are segmented in the range image to obtain robust feature points for the registration stage. After performing two-stage scan-matching, we use the extracted ground points to impose ground constraints and leverage the local feature submap for loop closure detection. By employing various data reuse strategies, we minimize memory consumption, creating a compact and accurate system. Experiments on multiple publicly available datasets demonstrate that our system provides more accurate localization and higher-quality maps compared to other state-of-the-art methods. © 2024 IEEE.

关键词： Mapping

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Probabilistic Visibility-Aware Trajectory Planning for Target Tracking in Cluttered Environments

Probabilistic Visibility-Aware Trajectory Planning for Targe...

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American Control Conference (ACC)

作者： Han Gao W. Pengying S. Yao Kangjie Zhou M. Ji Hangxin Liu Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI)

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Target tracking has numerous significant civilian and military applications, and maintaining the visibility of the target plays a vital role in ensuring the success of the tracking task. Existing visibility-aware planners primarily focus on keeping the target within the limited field of view of an onboard sensor and avoiding obstacle occlusion. However, the negative impact of system uncertainty is often neglected, rendering the planners delicate to uncertainties in practice. To bridge the gap, this work proposes a model predictive control (MPC)-based trajectory planner for real-time visibility-aware and safe target tracking in the presence of system uncertainty. For more accurate target motion prediction, we introduce the concept of belief-space probability of detection (BPOD) to measure the predictive visibility of the target under stochastic robot and target states. An Extended Kalman Filter variant incorporating BPOD is developed to predict target belief state under uncertain visibility within the planning horizon. To reach real-time trajectory planning, we propose a computationally efficient algorithm to uniformly calculate both BPOD and the chance-constrained collision risk by utilizing linearized signed distance function (SDF), and subsequently solve the MPC problem by sequential convex programming. Extensive simulation results with benchmark comparisons show the capacity of the proposed approach to robustly maintain the visibility of the target under high system uncertainty. The practicality of the proposed trajectory planner is validated by real-world experiments.

关键词： Uncertainty Target tracking Trajectory planning Stochastic systems Simulation Stochastic processes Robot sensing systems

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An Underdetermined Wideband 2-D DOA Estimation Algorithm via Acoustic Vector-Sensor Coprime Array

An Underdetermined Wideband 2-D DOA Estimation Algorithm via...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Han, Xueyan Liu, Meiqin Zhang, Senlin College of Information Engineering Binzhou University Binzhou China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xian China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350316728

In this paper, the issue of underdetermined two-dimensional (2-D) direction-of-arrival (DOA) estimation of underwater wideband signals is investigated by using an acoustic vector-sensor extended coprime array. Firstly, a novel virtual extended acoustic vector-sensor array is constructed and the model of the received wideband signals is derived under this array. Then, a binary selection matrix is designed to decrease the computational complexity by reducing dimensions. Finally, an accurate wideband DOA estimation is achieved by solving an optimization problem based on joint sparse recovery theory in the virtual domain, which fully uses the joint sparsity between different sub-bands. Some experimental simulations are carried out to confirm the effectiveness of the proposed algorithm. Some experimental simulations are carried out to confirm the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Direction of arrival

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Harmony: Heterogeneous Multi-Modal Federated Learning through Disentangled Model Training 23

Harmony: Heterogeneous Multi-Modal Federated Learning throug...

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21st Annual International Conference on Mobile Systems, Applications and Services, MobiSys 2023

作者： Ouyang, Xiaomin Xie, Zhiyuan Fu, Heming Cheng, Sitong Pan, Li Ling, Neiwen Xing, Guoliang Zhou, Jiayu Huang, Jianwei Chinese University of Hong Kong Hong Kong Michigan State University United States Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9798400701108

Multi-modal sensing systems are increasingly prevalent in real-world applications such as health monitoring and autonomous driving. Most multi-modal learning approaches need to access users' raw data, which poses significant concerns to users' privacy. Federated learning (FL) provides a privacy-aware distributed learning framework. However, current FL approaches have not addressed the unique challenges of heterogeneous multi-modal FL systems, such as modality heterogeneity and significantly longer training delay. In this paper, we propose Harmony, a new system for heterogeneous multi-modal federated learning. Harmony disentangles the multi-modal network training in a novel two-stage framework, namely modality-wise federated learning and federated fusion learning. By integrating a novel balance-aware resource allocation mechanism in modality-wise FL and exploiting modality biases in federated fusion learning, Harmony improves the model accuracy under non-i.i.d. data distributions and speeds up system convergence. We implemented Harmony on a real-world multi-modal sensor testbed deployed in the homes of 16 elderly subjects for Alzheimer's Disease monitoring. Our evaluation on the testbed and three large-scale public datasets of different applications show that, Harmony outperforms by up to 46.35% accuracy over state-of-the-art baselines and saves up to 30% training delay. © 2023 Owner/Author(s).

关键词： Testbeds

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Learning Goal-oriented Bimanual Dough Rolling Using Dynamic Heterogeneous Graph Based on Human Demonstration

Learning Goal-oriented Bimanual Dough Rolling Using Dynamic ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Liu, Junjia Li, Chenzui Wang, Shixiong Dong, Zhipeng Lam, Tin Lun Calinon, Sylvain Li, Miao Chen, Fei T-Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong School of Science and Engineering The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Sylvain Calinon is with the Idiap Research Institute Martigny Switzerland School of Microelectronics Institute of Technological Sciences Wuhan University Wuhan China

ISBN: (纸本)9781665481090

Soft object manipulation poses significant chal-lenges for robots, requiring effective techniques for state representation and manipulation policy learning. State representation involves capturing the dynamic changes in the environment, while manipulation policy learning focuses on establishing the relationship between robot actions and state transformations to achieve specific goals. To address these challenges, this research paper introduces a novel approach: a dynamic heterogeneous graph-based model for learning goal-oriented soft object manipulation policies. The proposed model utilizes graphs as a unified representation for both states and policy learning. By leveraging the dynamic graph, we can extract crucial information regarding object dynamics and manipulation policies. Furthermore, the model facilitates the integration of demonstrations, enabling guided policy learning. To evaluate the efficacy of our approach, we designed a dough rolling task and conducted experiments using both a differentiable simulator and a real-world humanoid robot. Additionally, several ablation studies were performed to analyze the effect of our method, demonstrating its superiority in achieving human-like behavior. © 2024 IEEE.

关键词： Adversarial machine learning

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SEMANTIC FLOW: LEARNING SEMANTIC FIELDS OF DYNAMIC SCENES FROM MONOCULAR VIDEOS 12

SEMANTIC FLOW: LEARNING SEMANTIC FIELDS OF DYNAMIC SCENES FR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Tian, Fengrui Duan, Yueqi Wang, Angtian Guo, Jianfei Du, Shaoyi National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Tsinghua University China Johns Hopkins University United States Shanghai AI Laboratory China

In this work, we pioneer Semantic Flow, a neural semantic representation of dynamic scenes from monocular videos. In contrast to previous NeRF methods that reconstruct dynamic scenes from the colors and volume densities of individual points, Semantic Flow learns semantics from continuous flows that contain rich 3D motion information. As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames. More specifically, we first learn a flow network to predict flows in the dynamic scene, and propose a flow feature aggregation module to extract flow features from video frames. Then, we propose a flow attention module to extract motion information from flow features, which is followed by a semantic network to output semantic logits of flows. We integrate the logits with volume densities in the viewing direction to supervise the flow features with semantic labels on video frames. Experimental results show that our model is able to learn from multiple dynamic scenes and supports a series of new tasks such as instance-level scene editing, semantic completions, dynamic scene tracking and semantic adaption on novel scenes. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Semantics

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Happy: A Debiased Learning Framework for Continual Generalized Category Discovery 38

Happy: A Debiased Learning Framework for Continual Generaliz...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploring Intermittent Dynamics of Neural Activity in a Single-Pair System of Excitatory and Inhibitory Neurons 3

Exploring Intermittent Dynamics of Neural Activity in a Sing...

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3rd International Conference on Emerging Techniques in Computational intelligence, ICETCI 2023

作者： Sugawara, Akio Nobukawa, Sou Wagatsuma, Nobuhiko Inagaki, Keiichiro Chiba Institute of Technology Department of Computer Science Narashino Japan Toho University Department of Information Science Funabashi Japan Chubu University Department of Artificial Intelligence and Robotics Kasugai Japan

ISBN: (纸本)9798350300604

A long-Tailed property has been observed at various levels of the brain, ranging from neural population activity to the level of cognitive neurodynamics. Specifically, at the cognitive neurodynamics level, a phenomenon called perceptual alternation displays perceptual durations that follow a gamma or log-normal distribution. Moreover, this perceptual alternation becomes nondeterministic. The occurrence of alternation phenomena has also been noticed in neural populations at the cognitive neurodynamics level. Even in a system consisting of a single pair of excitatory and inhibitory neurons, referred to as chaos-chaos intermittency (CCI), a similar intermittent alternation of neural activity emerges, involving intermittent transitions between multiple isolated attractors. In this study, our hypothesis was that nondeterminism and long-Tailed properties can emerge in this single-pair system. To test this hypothesis, we evaluated the determinism of two types of dynamics in the system that couples excitatory and inhibitory neurons: 1) transitions between attractors and 2) behavior within attractors. This evaluation was performed on multiple time scales using multi-scale entropy analysis (MSE) and an iterated amplitude-Adjusted Fourier transform (IAAFT) surrogate. The results demonstrated that the transitions between attractors were nondeterministic, while the behavior within attractors was deterministic. Furthermore, a long-Tailed property manifested in the duration of the transitions between attractors, possibly resulting from long-Term evaluation in the chaotic unstable orbit. The emergence of this long-Tailed property was attributed to the low frequency CCI around the attractor-merging bifurcation. This discovery contributes to the understanding of the long-Tailed properties exhibited by neural activity across multiple levels. © 2023 IEEE.

关键词： Brain

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