pwc-archive/papers-with-abstracts huggingface dataset:
Towards Semantic 3D Hand-Object Interaction Generation via Functional Text Guidance
Authors: Yongqi Tian, Xueyu Sun, Haoyuan He, Linji Hao, Ning Ding, Caigui Jiang
Date: 2025-02-28 00:00:00
Tasks: Object

Hand-object interaction(HOI) is the fundamental link between human and environment, yet its dexterous and complex pose significantly challenges for gesture control. Despite significant advances in AI and robotics, enabling machines to understand and simulate hand-object interactions, capturing the semantics of functional grasping tasks remains a considerable challenge. While previous work can generate stable and correct 3D grasps, they are still far from achieving functional grasps due to unconsidered grasp semantics. To address this challenge, we propose an innovative two-stage framework, Functional Grasp Synthesis Net (FGS-Net), for generating 3D HOI driven by functional text. This framework consists of a text-guided 3D model generator, Functional Grasp Generator (FGG), and a pose optimization strategy, Functional Grasp Refiner (FGR). FGG generates 3D models of hands and objects based on text input, while FGR fine-tunes the poses using Object Pose Approximator and energy functions to ensure the relative position between the hand and object aligns with human intent and remains physically plausible. Extensive experiments demonstrate that our approach achieves precise and high-quality HOI generation without requiring additional 3D annotation data.
Characteristics Analysis of Autonomous Vehicle Pre-crash Scenarios
Authors: Yixuan Li, Xuesong Wang, Tianyi Wang, Qian Liu
Date: 2025-02-28 00:00:00

To date, hundreds of crashes have occurred in open road testing of automated vehicles (AVs), highlighting the need for improving AV reliability and safety. Pre-crash scenario typology classifies crashes based on vehicle dynamics and kinematics features. Building on this, characteristics analysis can identify similar features under comparable crashes, offering a more effective reflection of general crash patterns and providing more targeted recommendations for enhancing AV performance. However, current studies primarily concentrated on crashes among conventional human-driven vehicles, leaving a gap in research dedicated to in-depth AV crash analyses. In this paper, we analyzed the latest California AV collision reports and used the newly revised pre-crash scenario typology to identify pre-crash scenarios. We proposed a set of mapping rules for automatically extracting these AV pre-crash scenarios, successfully identifying 24 types with a 98.1% accuracy rate, and obtaining two key scenarios of AV crashes (i.e., rear-end scenarios and intersection scenarios) through detailed analysis. Association analyses of rear-end scenarios showed that the significant environmental influencing factors were traffic control type, location type, light, etc. For intersection scenarios prone to severe crashes with detailed descriptions, we employed causal analyses to obtain the significant causal factors: habitual violations and expectations of certain behavior. Optimization recommendations were then formulated, addressing both governmental oversight and AV manufacturers' potential improvements. The findings of this paper could guide government authorities to develop related regulations, help manufacturers design AV test scenarios, and identify potential shortcomings in control algorithms specific to various real-world scenarios, thereby optimizing AV systems effectively.
Indoor Localization for Autonomous Robot Navigation
Authors: Sean Kouma, Rachel Masters
Date: 2025-02-28 00:00:00
Tasks: Indoor Localization, Navigate, Robot Navigation

Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.
EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar
Authors: Pengyu Zhang, Xieyuanli Chen, Yuwei Chen, Beizhen Bi, Zhuo Xu, Tian Jin, Xiaotao Huang, Liang Shen
Date: 2025-02-28 00:00:00
Tasks: Computational Efficiency, GPR

Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle. However, existing methods are primarily focused on small-scale place recognition (PR), leaving the challenges of PR in large-scale maps unaddressed. These challenges include the inherent sparsity of underground features and the variability in underground dielectric constants, which complicate robust localization. In this work, we investigate the geometric relationship between GPR echo sequences and underground scenes, leveraging the robustness of directional features to inform our network design. We introduce learnable Gabor filters for the precise extraction of directional responses, coupled with a direction-aware attention mechanism for effective geometric encoding. To further enhance performance, we incorporate a shift-invariant unit and a multi-scale aggregation strategy to better accommodate variations in di-electric constants. Experiments conducted on public datasets demonstrate that our proposed EDENet not only surpasses existing solutions in terms of PR performance but also offers advantages in model size and computational efficiency.
Subtask-Aware Visual Reward Learning from Segmented Demonstrations
Authors: Changyeon Kim, Minho Heo, Doohyun Lee, Jinwoo Shin, Honglak Lee, Joseph J. Lim, Kimin Lee
Date: 2025-02-28 00:00:00
Tasks: Contrastive Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring extensive trial-and-error and access to target behavior information, often unavailable in real-world settings. This paper introduces REDS: REward learning from Demonstration with Segmentations, a novel reward learning framework that leverages action-free videos with minimal supervision. Specifically, REDS employs video demonstrations segmented into subtasks from diverse sources and treats these segments as ground-truth rewards. We train a dense reward function conditioned on video segments and their corresponding subtasks to ensure alignment with ground-truth reward signals by minimizing the Equivalent-Policy Invariant Comparison distance. Additionally, we employ contrastive learning objectives to align video representations with subtasks, ensuring precise subtask inference during online interactions. Our experiments show that REDS significantly outperforms baseline methods on complex robotic manipulation tasks in Meta-World and more challenging real-world tasks, such as furniture assembly in FurnitureBench, with minimal human intervention. Moreover, REDS facilitates generalization to unseen tasks and robot embodiments, highlighting its potential for scalable deployment in diverse environments.
Map Space Belief Prediction for Manipulation-Enhanced Mapping
Authors: Joao Marcos Correia Marques, Nils Dengler, Tobias Zaenker, Jesper Mucke, Shenlong Wang, Maren Bennewitz, Kris Hauser
Date: 2025-02-28 00:00:00
Tasks: Decision Making Under Uncertainty, Prediction

Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Although Partially Observable Markov Decision Processes~(POMDPs) are standard for decision-making under uncertainty, representing unstructured interactive worlds remains challenging in this formalism. To tackle this, we define a POMDP whose belief is summarized by a metric-semantic grid map and propose a novel framework that uses neural networks to perform map-space belief updates to reason efficiently and simultaneously about object geometries, locations, categories, occlusions, and manipulation physics. Further, to enable accurate information gain analysis, the learned belief updates should maintain calibrated estimates of uncertainty. Therefore, we propose Calibrated Neural-Accelerated Belief Updates (CNABUs) to learn a belief propagation model that generalizes to novel scenarios and provides confidence-calibrated predictions for unknown areas. Our experiments show that our novel POMDP planner improves map completeness and accuracy over existing methods in challenging simulations and successfully transfers to real-world cluttered shelves in zero-shot fashion.
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection
Authors: Hyeonuk Nam, Yong-Hwa Park
Date: 2025-02-28 00:00:00
Tasks: Boundary Detection, Event Detection, Representation Learning, Self-Supervised Learning, Sound Event Detection

Sound event detection (SED) has significantly benefited from self-supervised learning (SSL) approaches, particularly masked audio transformer for SED (MAT-SED), which leverages masked block prediction to reconstruct missing audio segments. However, while effective in capturing global dependencies, masked block prediction disrupts transient sound events and lacks explicit enforcement of temporal order, making it less suitable for fine-grained event boundary detection. To address these limitations, we propose JiTTER (Jigsaw Temporal Transformer for Event Reconstruction), an SSL framework designed to enhance temporal modeling in transformer-based SED. JiTTER introduces a hierarchical temporal shuffle reconstruction strategy, where audio sequences are randomly shuffled at both the block-level and frame-level, forcing the model to reconstruct the correct temporal order. This pretraining objective encourages the model to learn both global event structures and fine-grained transient details, improving its ability to detect events with sharp onset-offset characteristics. Additionally, we incorporate noise injection during block shuffle, providing a subtle perturbation mechanism that further regularizes feature learning and enhances model robustness. Experimental results on the DESED dataset demonstrate that JiTTER outperforms MAT-SED, achieving a 5.89% improvement in PSDS, highlighting the effectiveness of explicit temporal reasoning in SSL-based SED. Our findings suggest that structured temporal reconstruction tasks, rather than simple masked prediction, offer a more effective pretraining paradigm for sound event representation learning.
Weakly Supervised Multiple Instance Learning for Whale Call Detection and Temporal Localization in Long-Duration Passive Acoustic Monitoring
Authors: Ragib Amin Nihal, Benjamin Yen, Runwu Shi, Kazuhiro Nakadai
Date: 2025-02-28 00:00:00
Tasks: Multiple Instance Learning, Temporal Localization

Marine ecosystem monitoring via Passive Acoustic Monitoring (PAM) generates vast data, but deep learning often requires precise annotations and short segments. We introduce DSMIL-LocNet, a Multiple Instance Learning framework for whale call detection and localization using only bag-level labels. Our dual-stream model processes 2-30 minute audio segments, leveraging spectral and temporal features with attention-based instance selection. Tests on Antarctic whale data show longer contexts improve classification (F1: 0.8-0.9) while medium instances ensure localization precision (0.65-0.70). This suggests MIL can enhance scalable marine monitoring. Code: https://github.com/Ragib-Amin-Nihal/DSMIL-Loc
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
Authors: Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima, Baris Kasikci
Date: 2025-02-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Low-rank compression, speech-recognition, Speech Recognition

Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in the reduced dimension. Evaluation results show that our method can compress Whisper large-v3's encoder size by over 50%, matching Whisper medium's size with better transcription accuracy, thereby establishing a new Pareto-optimal frontier of efficiency and performance. The code of LiteASR is available at https://github.com/efeslab/LiteASR.
Will AI replace Software Engineers? Do not hold your breath
Authors: Abhik Roychoudhury, Andreas Zeller
Date: 2025-02-27 00:00:00

Artificial Intelligence (AI) technology such as Large Language Models (LLMs) have become extremely popular in creating code. This has led to the conjecture that future software jobs will be exclusively conducted by LLMs, and the software industry will cease to exist. But software engineering is much more than producing code -- notably, \emph{maintaining} large software and keeping it reliable is a major part of software engineering, which LLMs are not yet capable of.
Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems
Authors: Faisal Mohammad, Duksan Ryu
Date: 2025-02-28 00:00:00
Tasks: Autonomous Driving

In recent years, the rise of autonomous driving technologies has highlighted the critical importance of reliable software for ensuring safety and performance. This paper proposes a novel approach for just-in-time software defect prediction (JIT-SDP) in autonomous driving software systems using multimodal learning. The proposed model leverages the multimodal transformers in which the pre-trained transformers and a combining module deal with the multiple data modalities of the software system datasets such as code features, change metrics, and contextual information. The key point for adapting multimodal learning is to utilize the attention mechanism between the different data modalities such as text, numerical, and categorical. In the combining module, the output of a transformer model on text data and tabular features containing categorical and numerical data are combined to produce the predictions using the fully connected layers. Experiments conducted on three open-source autonomous driving system software projects collected from the GitHub repository (Apollo, Carla, and Donkeycar) demonstrate that the proposed approach significantly outperforms state-of-the-art deep learning and machine learning models regarding evaluation metrics. Our findings highlight the potential of multimodal learning to enhance the reliability and safety of autonomous driving software through improved defect prediction.
A Survey of Link Prediction in Temporal Networks
Authors: Jiafeng Xiong, Ahmad Zareie, Rizos Sakellariou
Date: 2025-02-28 00:00:00
Tasks: Link Prediction, Prediction, Survey

Temporal networks have gained significant prominence in the past decade for modelling dynamic interactions within complex systems. A key challenge in this domain is Temporal Link Prediction (TLP), which aims to forecast future connections by analysing historical network structures across various applications including social network analysis. While existing surveys have addressed specific aspects of TLP, they typically lack a comprehensive framework that distinguishes between representation and inference methods. This survey bridges this gap by introducing a novel taxonomy that explicitly examines representation and inference from existing methods, providing a novel classification of approaches for TLP. We analyse how different representation techniques capture temporal and structural dynamics, examining their compatibility with various inference methods for both transductive and inductive prediction tasks. Our taxonomy not only clarifies the methodological landscape but also reveals promising unexplored combinations of existing techniques. This taxonomy provides a systematic foundation for emerging challenges in TLP, including model explainability and scalable architectures for complex temporal networks.
State-Dependent Conformal Perception Bounds for Neuro-Symbolic Verification of Autonomous Systems
Authors: Thomas Waite, Yuang Geng, Trevor Turnquist, Ivan Ruchkin, Radoslav Ivanov
Date: 2025-02-28 00:00:00
Tasks: Conformal Prediction

It remains a challenge to provide safety guarantees for autonomous systems with neural perception and control. A typical approach obtains symbolic bounds on perception error (e.g., using conformal prediction) and performs verification under these bounds. However, these bounds can lead to drastic conservatism in the resulting end-to-end safety guarantee. This paper proposes an approach to synthesize symbolic perception error bounds that serve as an optimal interface between perception performance and control verification. The key idea is to consider our error bounds to be heteroskedastic with respect to the system's state -- not time like in previous approaches. These bounds can be obtained with two gradient-free optimization algorithms. We demonstrate that our bounds lead to tighter safety guarantees than the state-of-the-art in a case study on a mountain car.
Reproducible Optical Tracking Precision: Evaluating a Static, Near-Parallel Support Structure for OptiTrack PrimeX22 Cameras
Authors: Oliver Krumpek, Ole Kroeger, Sebastian Mohr
Date: 2025-02-28 00:00:00

This paper presents the design and evaluation of a physical support structure for the OptiTrack X22 tracking systems, constructed from carbon fiber-reinforced polymer (CFRP) and Invar steel. These materials were chosen for their low thermal expansion, ensuring geometric stability and rigidity necessary for accurate spatial measurements. The support system is scalable and adaptable for various applications and setups. The study further investigates the effects of camera placement and separation in near-parallel configurations on measurement accuracy and precision. Experimental results show a significant correlation between camera distance and measurement precision - closer camera setups yield higher precision. The optimized camera arrangement allowed the prototype to achieve accuracies of +/-0.74 mm along the camera's line of sight and +/-0.12 mm in orthogonal directions. The experiments show that the standard deviation of the noise on a single measurement plane orthogonal to the camera's line of sight vary between 0.02 and 0.07, indicating that the measurement noise is not constant for every point on that specific plane in the meanurement space. Details of the system's design and validation are provided to enhance reproducibility and encourage further development in areas like industrial automation and medical device tracking. By delivering a modular solution with validated accuracy, this work aims to promote innovation and practical application in precision tracking technology, facilitating broader adoption and iterative improvements. This approach enhances the accessibility and versatility of high-precision tracking technology, supporting future progress in the field.
Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids
Authors: Abdallah Alalem Albustami, Ahmad F. Taha, Elias Bou-Harb
Date: 2025-02-28 00:00:00
Tasks: Intrusion Detection

Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats. This paper presents the first attempt to design and assess stealthy false data injection attacks (FDIAs) against nonlinear differential algebraic equation (NDAE) models of power networks. NDAE models, favored in industry for their ability to accurately capture both dynamic and steady-state behaviors, provide a more accurate representation of power system behavior by coupling dynamic and algebraic states. We propose novel FDIA strategies that simultaneously evade both dynamic and static intrusion detection systems while respecting the algebraic power flow and operational constraints inherent in NDAE models. We demonstrate how the coupling between dynamic and algebraic states in NDAE models significantly restricts the attacker's ability to manipulate state estimates while maintaining stealthiness. This highlights the importance of using more comprehensive power system models in cybersecurity analysis and reveals potential vulnerabilities that may be overlooked in simplified representations. The proposed attack strategies are validated through simulations on the IEEE 39-bus system.
A general partitioning strategy for non-centralized control
Authors: Alessandro Riccardi, Luca Laurenti, Bart De Schutter
Date: 2025-02-28 00:00:00
Tasks: Model Predictive Control

Partitioning is a fundamental challenge for non-centralized control of large-scale systems, such as hierarchical, decentralized, distributed, and coalitional strategies. The problem consists of finding a decomposition of a network of dynamical systems into system units for which local controllers can be designed. Unfortunately, despite its critical role, a generalized approach to partitioning applicable to every system is still missing from the literature. This paper introduces a novel partitioning framework that integrates an algorithmic selection of fundamental system units (FSUs), considered indivisible entities, with an aggregative procedure, either algorithmic or optimization-based, to select composite system units (CSUs) made of several FSUs. A key contribution is the introduction of a global network metric, the partition index, which quantitatively balances intra- and inter-CSU interactions, with a granularity parameter accounting for the size of CSUs, allowing for their selection at different levels of aggregation. The proposed method is validated through case studies in distributed model predictive control (DMPC) for linear and hybrid systems, showing significant reductions in computation time and cost while maintaining or improving control performance w.r.t. conventional strategies.
Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach
Authors: Max van Haren, Lennart Blanken, Tom Oomen
Date: 2025-02-28 00:00:00

The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paper is to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function estimation in conjunction with iterative learning to directly learn parameter-varying feedforward control from data. This approach enables high tracking performance for feedforward control of linear parameter-varying dynamics, providing flexibility to varying reference tasks. The developed framework is validated on a benchmark industrial experimental setup featuring a belt-driven carriage.