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arXiv 2025年

作者： Qadri, Mohamad Swamy, Gokul Francis, Jonathan Kaess, Michael Bajcsy, Andrea Carnegie Mellon University Robotics Institute United States Bosch Center for Artificial Intelligence United States

Inverse Constraint Learning (ICL) is the problem of inferring constraints from safe (i.e., constraint-satisfying) demonstrations. The hope is that these inferred constraints can then be used downstream to search for safe policies for new tasks and, potentially, under different dynamics. Our paper explores the question of what mathematical entity ICL recovers. Somewhat surprisingly, we show that both in theory and in practice, ICL recovers the set of states where failure is inevitable, rather than the set of states where failure has already happened. In the language of safe control, this means we recover a backwards reachable tube (BRT) rather than a failure set. In contrast to the failure set, the BRT depends on the dynamics of the data collection system. We discuss the implications of the dynamics-conditionedness of the recovered constraint on both the sample-efficiency of policy search and the transferability of learned constraints. © 2025, CC BY.

关键词： Contrastive Learning

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Group Decision-Making in Robot Teleoperation: Two Heads are Better Than One 20

Group Decision-Making in Robot Teleoperation: Two Heads are ...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Nguyen, Duc-An Bhattacharyya, Raunak Colombatto, Clara Fleming, Steve Posner, Ingmar Hawes, Nick Oxford Robotics Institute University of Oxford Oxford United Kingdom Yardi School of Artificial Intelligence Indian Institute of Technology Delhi New Delhi India University of Waterloo Department of Psychology ON Canada University College London Department of Experimental Psychology London United Kingdom

ISBN: (纸本)9798350378931

Operators working with robots in safety-critical domains have to make decisions under uncertainty, which remains a challenging problem for a single human operator. An open question is whether two human operators can make better decisions jointly, as compared to a single operator alone. While prior work has shown that two heads are better than one, such studies have been mostly limited to static and passive tasks. We investigate joint decision-making in a dynamic task involving humans teleoperating robots. We conduct a human-subject experiment with N = 100 participants where each participant performed a navigation task with two mobiles robots in simulation. We find that joint decision-making through confidence sharing improves dyad performance beyond the better-performing individual (p © 2025 IEEE.

关键词： Remote control

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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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Research on underwater target tracking method based on IPSO-LSTM

Research on underwater target tracking method based on IPSO-...

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2024 International Conference on Mechatronic Engineering and artificial intelligence, MEAI 2024

作者： Guo, Xaingyun Liu, Kaizhou Wang, Yanhong School of Artificial Intelligence Shenyang University of Technology Liaoning Province Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Province Shenyang110016 China

ISBN: (纸本)9781510689176

When tracking underwater targets, due to the limited performance of detection equipment and complex environment interference, the target data is lost in the transmission process, which affects the accuracy of target trajectory prediction. The traditional target trajectory prediction method for establishing mathematical model has some limitations and difficulty, which can not meet the requirements of real-time and accurate target trajectory prediction in underwater field. An IPSO-LSTM trajectory prediction model for underwater targets is proposed. In this method, the motion characteristics of underwater targets are analyzed, and the learning rate of LSTM is optimized by optimizing the inertia weight of particle swarm and improving the learning factor. The real trajectory data of Autonomous Underwater Vehicle were used for experimental and comparative analysis. The results show that IPSO-LSTM combined with PSO has higher prediction accuracy and better fitting effect than LSTM and PSO-LSTM. The proposed model provides a new idea for underwater moving target tracking, which has better accuracy and stability. © 2025 SPIE.

关键词： Aircraft detection

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Layer-Animate for Transparent Video Generation

Layer-Animate for Transparent Video Generation

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

作者： Jingqi Bai Jingkai Zhou Benzhi Wang Weihua Chen Yang Yang Zhen Lei Fan Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Alibaba Group Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Transparent videos with alpha channels play a crucial role in film production, advertising, and augmented reality fields. However, there is currently no available method for producing transparent videos. Traditional methods are time-consuming and labor-intensive, and employing alternative approaches for this task will result in inaccurate transparent regions, constrained motion, and artifacts. To address these challenges, we propose Layer-Animate, the first method capable of generating transparent videos. Our method comprises two stages: in the first stage, transparent images are generated as the base images to provide content and transparency information for the next stage. In the second stage, Inter-Frame Attention is applied to decouple content from motion, enabling the motion module to focus better on action. Layer-Animate is the first method used to generate transparent videos with accurate transparent regions, sufficient motion, and no artifacts, as demonstrated by notable improvements in qualitative and quantitative metrics.

关键词： Accuracy Production Signal processing Acoustic measurements Robustness Acoustics Speech processing Advertising Augmented reality Videos

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ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images

arXiv

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arXiv 2025年

作者： Shen, Yanqing Tuna, Turcan Hutter, Marco Cadena, Cesar Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China Robotic Systems Lab ETH Zurich Switzerland

Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely under-explored. Furthermore, forests present particular challenges due to high self-similarity and substantial variations in vegetation growth over time. In this work, we propose a robust LiDAR-based place recognition method for natural forests, ForestLPR. We hypothesize that a set of cross-sectional images of the forest’s geometry at different heights contains the information needed to recognize revisiting a place. The cross-sectional images are represented by bird’s-eye view (BEV) density images of horizontal slices of the point cloud at different heights. Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors and introduces a multi-BEV interaction module to attend to information at different heights adaptively. It is followed by an aggregation layer that produces a rotation-invariant place descriptor. We evaluated the efficacy of our method extensively on real-world data from public benchmarks as well as robotic datasets and compared it against the state-of-the-art (SOTA) methods. The results indicate that ForestLPR has consistently good performance on all evaluations and achieves an average increase of 7.38% and 9.11% on Recall@1 over the closest competitor on intra-sequence loop closure detection and inter-sequence re-localization, respectively, validating our hypothesis1 Copyright © 2025, The Authors. All rights reserved.

关键词： Forest ecology

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Data-efficient LLM Fine-tuning for Code Generation

arXiv

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arXiv 2025年

作者： Lv, Weijie Xia, Xuan Huang, Sheng-Jun Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Large language models (LLMs) have demonstrated significant potential in code generation tasks. However, there remains a performance gap between open-source and closed-source models. To address this gap, existing approaches typically generate large amounts of synthetic data for fine-tuning, which often leads to inefficient training. In this work, we propose a data selection strategy in order to improve the effectiveness and efficiency of training for code-based LLMs. By prioritizing data complexity and ensuring that the sampled subset aligns with the distribution of the original dataset, our sampling strategy effectively selects high-quality data. Additionally, we optimize the tokenization process through a "dynamic pack" technique, which minimizes padding tokens and reduces computational resource consumption. Experimental results show that when training on 40% of the OSS-Instruct dataset, the DeepSeek-Coder-Base-6.7B model achieves an average performance of 66.9%, surpassing the 66.1% performance with the full dataset. Moreover, training time is reduced from 47 minutes to 34 minutes, and the peak GPU memory usage decreases from 61.47 GB to 42.72 GB during a single epoch. Similar improvements are observed with the CodeLlama-Python-7B model on the Evol-Instruct dataset. By optimizing both data selection and tokenization, our approach not only improves model performance but also enhances training efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

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Towards Conscious Service Robots

arXiv

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arXiv 2025年

作者： Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Deep learning’s success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain’s System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem solving

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BrainCLIP: Brain Representation via CLIP for Generic Natural Visual Stimulus Decoding

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ma, Yongqiang Liu, Yulong Chen, Liangjun Zhu, Guibo Chen, Badong Zheng, Nanning Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Shaanxi Xi'an710049 China Institute of Automation Beijing China

Functional Magnetic Resonance Imaging (fMRI) presents challenges due to limited paired samples and low signal-to-noise ratios, particularly in tasks involving reconstructing natural images or decoding their semantic content. To address these challenges, we introduce BrainCLIP, an innovative fMRI-based brain decoding model. BrainCLIP leverages Contrastive Language-Image Pre-training's (CLIP) cross-modal generalization abilities to bridge brain activity, images, and text for the first time. Our experiments demonstrate CLIP's effectiveness in diverse brain decoding tasks, including zero-shot visual category decoding, fMRI-image/text alignment, and fMRI-to-image generation. The core objective of BrainCLIP is to train a mapping network that translates fMRI patterns into a unified CLIP embedding space, achieved through visual and textual supervision integration. Our experiments highlight that this approach significantly enhances performance in tasks such as fMRI-text alignment and fMRI-based image generation. Notably, BrainCLIP surpasses BraVL, a recent multi-modal method, in zero-shot visual category decoding. Moreover, BrainCLIP demonstrates strong capability in reconstructing visual stimuli with high semantic fidelity, competing favorably with state-of-the-art methods in capturing high-level semantic features during fMRI-based natural image reconstruction. © 1982-2012 IEEE.

关键词： Signal to noise ratio

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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IEEE/SICE International Symposium on System Integration

作者： Svetlana Seliunina Artem Otelepko Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available.

关键词： Reflectivity Adaptation models Laser radar Three-dimensional displays System integration Robot sensing systems Encoding Sensor systems Sensors Safety

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