pwc-archive/papers-with-abstracts huggingface dataset:
UnIRe: Unsupervised Instance Decomposition for Dynamic Urban Scene Reconstruction
Authors: Yunxuan Mao, Rong Xiong, Yue Wang, Yiyi Liao
Date: 2025-04-01 00:00:00
Tasks: 3DGS, Autonomous Driving

Reconstructing and decomposing dynamic urban scenes is crucial for autonomous driving, urban planning, and scene editing. However, existing methods fail to perform instance-aware decomposition without manual annotations, which is crucial for instance-level scene editing.We propose UnIRe, a 3D Gaussian Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances using only RGB images and LiDAR point clouds. At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space, enabling unsupervised instance separation based on spatiotemporal correlations. These 4D superpoints serve as the foundation for our decomposed 4D initialization, i.e., providing spatial and temporal initialization to train a dynamic 3DGS for arbitrary dynamic classes without requiring bounding boxes or object templates.Furthermore, we introduce a smoothness regularization strategy in both 2D and 3D space, further improving the temporal stability.Experiments on benchmark datasets show that our method outperforms existing methods in decomposed dynamic scene reconstruction while enabling accurate and flexible instance-level editing, making it a practical solution for real-world applications.
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
Authors: Alessandro Colombo, Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin
Date: 2025-04-01 00:00:00
Tasks: In-Context Learning

Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system nonlinearities and parameter uncertainties. In this work, we propose an in-context learning framework leveraging transformer-based models to perform zero-shot speed estimation using only electrical measurements. By training the filter offline on simulated motor trajectories, we enable real-time inference on unseen real motors without retraining, eliminating the need for explicit system identification while retaining adaptability to varying operating conditions. Experimental results demonstrate that our method outperforms traditional Kalman filter-based estimators, especially in low-speed regimes that are crucial during motor startup.
Robust LiDAR-Camera Calibration with 2D Gaussian Splatting
Authors: Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa, Takeshi Oishi
Date: 2025-04-01 00:00:00
Tasks: Camera Calibration

LiDAR-camera systems have become increasingly popular in robotics recently. A critical and initial step in integrating the LiDAR and camera data is the calibration of the LiDAR-camera system. Most existing calibration methods rely on auxiliary target objects, which often involve complex manual operations, whereas targetless methods have yet to achieve practical effectiveness. Recognizing that 2D Gaussian Splatting (2DGS) can reconstruct geometric information from camera image sequences, we propose a calibration method that estimates LiDAR-camera extrinsic parameters using geometric constraints. The proposed method begins by reconstructing colorless 2DGS using LiDAR point clouds. Subsequently, we update the colors of the Gaussian splats by minimizing the photometric loss. The extrinsic parameters are optimized during this process. Additionally, we address the limitations of the photometric loss by incorporating the reprojection and triangulation losses, thereby enhancing the calibration robustness and accuracy.
Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing
Authors: Camilo Gonzalez Arango, Houshyar Asadi, Mohammad Reza Chalak Qazani, Chee Peng Lim
Date: 2025-04-01 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Motion Cueing Algorithms (MCAs) encode the movement of simulated vehicles into movement that can be reproduced with a motion simulator to provide a realistic driving experience within the capabilities of the machine. This paper introduces a novel learning-based MCA for serial robot-based motion simulators. Building on the differentiable predictive control framework, the proposed method merges the advantages of Nonlinear Model Predictive Control (NMPC) - notably nonlinear constraint handling and accurate kinematic modeling - with the computational efficiency of machine learning. By shifting the computational burden to offline training, the new algorithm enables real-time operation at high control rates, thus overcoming the key challenge associated with NMPC-based motion cueing. The proposed MCA incorporates a nonlinear joint-space plant model and a policy network trained to mimic NMPC behavior while accounting for joint acceleration, velocity, and position limits. Simulation experiments across multiple motion cueing scenarios showed that the proposed algorithm performed on par with a state-of-the-art NMPC-based alternative in terms of motion cueing quality as quantified by the RMSE and correlation coefficient with respect to reference signals. However, the proposed algorithm was on average 400 times faster than the NMPC baseline. In addition, the algorithm successfully generalized to unseen operating conditions, including motion cueing scenarios on a different vehicle and real-time physics-based simulations.
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Authors: Yuanqi Yao, Siao Liu, Haoming Song, Delin Qu, Qizhi Chen, Yan Ding, Bin Zhao, Zhigang Wang, Xuelong Li, Dong Wang
Date: 2025-04-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Prompt Learning, Robot Manipulation, Transfer Learning

Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primitives. Within our two stage learning scheme, we first learn a set of primitive prompts to represent shared primitives through multi-skills pre-training stage, where motion-aware prompts are learned to capture semantic and motion shared primitives across different skills. Secondly, when acquiring new skills in lifelong span, new prompts are appended and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones. For evaluation, we construct a large-scale skill dataset and conduct extensive experiments in both simulation and real-world tasks, demonstrating PPL's superior performance over state-of-the-art methods.
Interpreting and Improving Optimal Control Problems with Directional Corrections
Authors: Trevor Barron, Xiaojing Zhang
Date: 2025-04-01 00:00:00

Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP's objective function. In practice, the objective function consists of a set of individual components that must be carefully modeled and traded off such that the OCP has the desired solution. It is often challenging to balance multiple components to achieve the desired solution and to understand, when the solution is undesired, the impact of individual cost components. In this paper, we present a framework addressing these challenges based on the concept of directional corrections. Specifically, given the solution to an OCP that is deemed undesirable, and access to an expert providing the direction of change that would increase the desirability of the solution, our method analyzes the individual cost components for their "consistency" with the provided directional correction. This information can be used to improve the OCP formulation, e.g., by increasing the weight of consistent cost components, or reducing the weight of - or even redesigning - inconsistent cost components. We also show that our framework can automatically tune parameters of the OCP to achieve consistency with a set of corrections.
Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
Authors: Anjian Li, Ryne Beeson
Date: 2025-04-01 00:00:00
Tasks: Collision Avoidance

Diffusion models have recently emerged as effective generative frameworks for trajectory optimization, capable of producing high-quality and diverse solutions. However, training these models in a purely data-driven manner without explicit incorporation of constraint information often leads to violations of critical constraints, such as goal-reaching, collision avoidance, and adherence to system dynamics. To address this limitation, we propose a novel approach that aligns diffusion models explicitly with problem-specific constraints, drawing insights from the Dynamic Data-driven Application Systems (DDDAS) framework. Our approach introduces a hybrid loss function that explicitly measures and penalizes constraint violations during training. Furthermore, by statistically analyzing how constraint violations evolve throughout the diffusion steps, we develop a re-weighting strategy that aligns predicted violations to ground truth statistics at each diffusion step. Evaluated on a tabletop manipulation and a two-car reach-avoid problem, our constraint-aligned diffusion model significantly reduces constraint violations compared to traditional diffusion models, while maintaining the quality of trajectory solutions. This approach is well-suited for integration into the DDDAS framework for efficient online trajectory adaptation as new environmental data becomes available.
Coarse-to-Fine Learning for Multi-Pipette Localisation in Robot-Assisted In Vivo Patch-Clamp
Authors: Lan Wei, Gema Vera Gonzalez, Phatsimo Kgwarae, Alexander Timms, Denis Zahorovsky, Simon Schultz, Dandan Zhang
Date: 2025-03-31 00:00:00
Tasks: Generative Adversarial Network

In vivo image-guided multi-pipette patch-clamp is essential for studying cellular interactions and network dynamics in neuroscience. However, current procedures mainly rely on manual expertise, which limits accessibility and scalability. Robotic automation presents a promising solution, but achieving precise real-time detection of multiple pipettes remains a challenge. Existing methods focus on ex vivo experiments or single pipette use, making them inadequate for in vivo multi-pipette scenarios. To address these challenges, we propose a heatmap-augmented coarse-to-fine learning technique to facilitate multi-pipette real-time localisation for robot-assisted in vivo patch-clamp. More specifically, we introduce a Generative Adversarial Network (GAN)-based module to remove background noise and enhance pipette visibility. We then introduce a two-stage Transformer model that starts with predicting the coarse heatmap of the pipette tips, followed by the fine-grained coordination regression module for precise tip localisation. To ensure robust training, we use the Hungarian algorithm for optimal matching between the predicted and actual locations of tips. Experimental results demonstrate that our method achieved > 98% accuracy within 10 {\mu}m, and > 89% accuracy within 5 {\mu}m for the localisation of multi-pipette tips. The average MSE is 2.52 {\mu}m.
Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors
Authors: Ilir Tahiraj, Jeremialie Swadiryus, Felix Fent, Markus Lienkamp
Date: 2025-03-31 00:00:00
Tasks: Autonomous Driving, Binary Classification, Contrastive Learning, Sensor Fusion

The goal of extrinsic calibration is the alignment of sensor data to ensure an accurate representation of the surroundings and enable sensor fusion applications. From a safety perspective, sensor calibration is a key enabler of autonomous driving. In the current state of the art, a trend from target-based offline calibration towards targetless online calibration can be observed. However, online calibration is subject to strict real-time and resource constraints which are not met by state-of-the-art methods. This is mainly due to the high number of parameters to estimate, the reliance on geometric features, or the dependence on specific vehicle maneuvers. To meet these requirements and ensure the vehicle's safety at any time, we propose a miscalibration detection framework that shifts the focus from the direct regression of calibration parameters to a binary classification of the calibration state, i.e., calibrated or miscalibrated. Therefore, we propose a contrastive learning approach that compares embedded features in a latent space to classify the calibration state of two different sensor modalities. Moreover, we provide a comprehensive analysis of the feature embeddings and challenging calibration errors that highlight the performance of our approach. As a result, our method outperforms the current state-of-the-art in terms of detection performance, inference time, and resource demand. The code is open source and available on https://github.com/TUMFTM/MiscalibrationDetection.
Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion
Authors: Artemii Redkin, Zdravko Dugonjic, Mike Lambeta, Roberto Calandra
Date: 2025-03-27 00:00:00

Vision-based tactile sensors use structured light to measure deformation in their elastomeric interface. Until now, vision-based tactile sensors such as DIGIT and GelSight have been using a single, static pattern of structured light tuned to the specific form factor of the sensor. In this work, we investigate the effectiveness of dynamic illumination patterns, in conjunction with image fusion techniques, to improve the quality of sensing of vision-based tactile sensors. Specifically, we propose to capture multiple measurements, each with a different illumination pattern, and then fuse them together to obtain a single, higher-quality measurement. Experimental results demonstrate that this type of dynamic illumination yields significant improvements in image contrast, sharpness, and background difference. This discovery opens the possibility of retroactively improving the sensing quality of existing vision-based tactile sensors with a simple software update, and for new hardware designs capable of fully exploiting dynamic illumination.
Gaze-Guided 3D Hand Motion Prediction for Detecting Intent in Egocentric Grasping Tasks
Authors: Yufei He, Xucong Zhang, Arno H. A. Stienen
Date: 2025-03-27 00:00:00
Tasks: motion prediction

Human intention detection with hand motion prediction is critical to drive the upper-extremity assistive robots in neurorehabilitation applications. However, the traditional methods relying on physiological signal measurement are restrictive and often lack environmental context. We propose a novel approach that predicts future sequences of both hand poses and joint positions. This method integrates gaze information, historical hand motion sequences, and environmental object data, adapting dynamically to the assistive needs of the patient without prior knowledge of the intended object for grasping. Specifically, we use a vector-quantized variational autoencoder for robust hand pose encoding with an autoregressive generative transformer for effective hand motion sequence prediction. We demonstrate the usability of these novel techniques in a pilot study with healthy subjects. To train and evaluate the proposed method, we collect a dataset consisting of various types of grasp actions on different objects from multiple subjects. Through extensive experiments, we demonstrate that the proposed method can successfully predict sequential hand movement. Especially, the gaze information shows significant enhancements in prediction capabilities, particularly with fewer input frames, highlighting the potential of the proposed method for real-world applications.
Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving
Authors: Chaofan Wu, Jiaheng Li, Jinghao Cao, Ming Li, Yongkang Feng, Jiayu Wu Shuwen Xu, Zihang Gao, Sidan Du, Yang Li
Date: 2025-03-26 00:00:00
Tasks: Autonomous Driving, Depth Estimation

Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniDepth-Occ. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras, and the data volume has reached twice that of SemanticKITTI. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.
AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
Authors: You-Le Fang, Dong-Shan Jian, Xiang Li, Yan-Qing Ma
Date: 2025-04-02 00:00:00
Tasks: scientific discovery

Current limitations in human scientific discovery necessitate a new research paradigm. While advances in artificial intelligence (AI) offer a highly promising solution, enabling AI to emulate human-like scientific discovery remains an open challenge. To address this, we propose AI-Newton, a concept-driven discovery system capable of autonomously deriving physical laws from raw data -- without supervision or prior physical knowledge. The system integrates a knowledge base and knowledge representation centered on physical concepts, along with an autonomous discovery workflow. As a proof of concept, we apply AI-Newton to a large set of Newtonian mechanics problems. Given experimental data with noise, the system successfully rediscovers fundamental laws, including Newton's second law, energy conservation and law of gravitation, using autonomously defined concepts. This achievement marks a significant step toward AI-driven autonomous scientific discovery.
Token Pruning in Audio Transformers: Optimizing Performance and Decoding Patch Importance
Authors: Taehan Lee, Hyukjun Lee
Date: 2025-04-02 00:00:00
Tasks: Audio Classification

Vision Transformers (ViTs) have achieved state-of-the-art performance across various computer vision tasks, but their high computational cost remains a challenge. Token pruning has been proposed to reduce this cost by selectively removing less important tokens. While effective in vision tasks by discarding non-object regions, applying this technique to audio tasks presents unique challenges, as distinguishing relevant from irrelevant regions in time-frequency representations is less straightforward. In this study, for the first time, we applied token pruning to ViT-based audio classification models using Mel-spectrograms and analyzed the trade-offs between model performance and computational cost: TopK token pruning can reduce MAC operations of AudioMAE and AST by 30-40%, with less than a 1% drop in classification accuracy. Our analysis reveals that while high-intensity tokens contribute significantly to model accuracy, low-intensity tokens remain important. In particular, they play a more critical role in general audio classification tasks than in speech-specific tasks.
Code Red! On the Harmfulness of Applying Off-the-shelf Large Language Models to Programming Tasks
Authors: Ali Al-Kaswan, Sebastian Deatc, Begüm Koç, Arie van Deursen, Maliheh Izadi
Date: 2025-04-02 00:00:00

Nowadays, developers increasingly rely on solutions powered by Large Language Models (LLM) to assist them with their coding tasks. This makes it crucial to align these tools with human values to prevent malicious misuse. In this paper, we propose a comprehensive framework for assessing the potential harmfulness of LLMs within the software engineering domain. We begin by developing a taxonomy of potentially harmful software engineering scenarios and subsequently, create a dataset of prompts based on this taxonomy. To systematically assess the responses, we design and validate an automatic evaluator that classifies the outputs of a variety of LLMs both open-source and closed-source models, as well as general-purpose and code-specific LLMs. Furthermore, we investigate the impact of models size, architecture family, and alignment strategies on their tendency to generate harmful content. The results show significant disparities in the alignment of various LLMs for harmlessness. We find that some models and model families, such as Openhermes, are more harmful than others and that code-specific models do not perform better than their general-purpose counterparts. Notably, some fine-tuned models perform significantly worse than their base-models due to their design choices. On the other side, we find that larger models tend to be more helpful and are less likely to respond with harmful information. These results highlight the importance of targeted alignment strategies tailored to the unique challenges of software engineering tasks and provide a foundation for future work in this critical area.
xML-workFlow: an end-to-end explainable scikit-learn workflow for rapid biomedical experimentation
Authors: Khoa A. Tran, John V. Pearson, Nicola Waddell
Date: 2025-04-02 00:00:00

Motivation: Building and iterating machine learning models is often a resource-intensive process. In biomedical research, scientific codebases can lack scalability and are not easily transferable to work beyond what they were intended. xML-workFlow addresses this issue by providing a rapid, robust, and traceable end-to-end workflow that can be adapted to any ML project with minimal code rewriting. Results: We show a practical, end-to-end workflow that integrates scikit-learn, MLflow, and SHAP. This template significantly reduces the time and effort required to build and iterate on ML models, addressing the common challenges of scalability and reproducibility in biomedical research. Adapting our template may save bioinformaticians time in development and enables biomedical researchers to deploy ML projects. Availability and implementation: xML-workFlow is available at https://github.com/MedicalGenomicsLab/xML-workFlow.
Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models
Authors: Rafael Giebisch, Ken E. Friedl, Lev Sorokin, Andrea Stocco
Date: 2025-04-01 00:00:00
Tasks: Benchmarking, Conversational Question Answering, Question Answering

In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them prone to errors such as hallucinations, i.e., inaccurate, fictitious, and therefore factually incorrect information. In this paper, we present an LLM-based methodology for the automatic factual benchmarking of in-car conversational systems. We instantiate our methodology with five LLM-based methods, leveraging ensembling techniques and diverse personae to enhance agreement and minimize hallucinations. We use our methodology to evaluate CarExpert, an in-car retrieval-augmented conversational question answering system, with respect to the factual correctness to a vehicle's manual. We produced a novel dataset specifically created for the in-car domain, and tested our methodology against an expert evaluation. Our results show that the combination of GPT-4 with the Input Output Prompting achieves over 90 per cent factual correctness agreement rate with expert evaluations, other than being the most efficient approach yielding an average response time of 4.5s. Our findings suggest that LLM-based testing constitutes a viable approach for the validation of conversational systems regarding their factual correctness.