pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous Localization and Mapping Using Active mmWave Sensing in 5G NR
Authors: Tao Du, Jie Yang, Fan Liu, Jiaxiang Guo, Shuqiang Xia, Chao-Kai Wen, Shi Jin
Date: 2025-07-07 00:00:00
Tasks: Loop Closure Detection, Point Cloud Generation, Point Cloud Registration, Simultaneous Localization and Mapping

Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.
RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles
Authors: David Hunt, Shaocheng Luo, Spencer Hallyburton, Shafii Nillongo, Yi Li, Tingjun Chen, Miroslav Pajic
Date: 2025-07-01 00:00:00
Tasks: Autonomous Navigation, Graph Neural Network, Point Cloud Generation, Raspberry Pi 5

Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.
Adaptive Multipath-Based SLAM for Distributed MIMO Systems
Authors: Xuhong LI, Benjamin J. B. Deutschmann, Erik Leitinger, Florian Meyer
Date: 2025-06-26 00:00:00
Tasks: Simultaneous Localization and Mapping

Localizing users and mapping the environment using radio signals is a key task in emerging applications such as reliable communications, location-aware security, and safety critical navigation. Recently introduced multipath-based simultaneous localization and mapping (MP-SLAM) can jointly localize a mobile agent and the reflective surfaces in radio frequency (RF) environments. Most existing MP-SLAM methods assume that map features and their corresponding RF propagation paths are statistically independent, which neglects inherent dependencies arising when a single reflective surface contributes to different propagation paths or when an agent communicates with more than one base station. Previous approaches that aim to fuse information across propagation paths are limited by their inability to perform ray tracing in environments with nonconvex geometries. In this paper, we propose a Bayesian MP-SLAM method for distributed MIMO systems that addresses this limitation. In particular, we use amplitude statistics to establish adaptive time-varying detection probabilities. Based on the resulting "soft" ray-tracing strategy, our method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation method for the joint estimation of map features and agent position is established by applying the message passing rules of the sum-product algorithm (SPA) to the factor graph that represents the proposed statistical model. We also introduce an improved proposal PDF for particle-based computation of SPA messages. This proposal PDF enables the early detection of new surfaces that are solely supported by double-bounce paths. Our method is validated using synthetic RF measurements in a challenging scenario with nonconvex geometries. The results demonstrate that it can provide accurate localization and mapping estimates as well as attain the posterior CRLB.
Ark: An Open-source Python-based Framework for Robot Learning
Authors: Magnus Dierking, Christopher E. Mower, Sarthak Das, Huang Helong, Jiacheng Qiu, Cody Reading, Wei Chen, Huidong Liang, Huang Guowei, Jan Peters, Quan Xingyue, Jun Wang, Haitham Bou-Ammar
Date: 2025-06-24 00:00:00
Tasks: Imitation Learning, Motion Planning

Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind progress in machine learning. A major bottleneck is software: current robot stacks demand steep learning curves, low-level C/C++ expertise, fragmented tooling, and intricate hardware integration, in stark contrast to the Python-centric, well-documented ecosystems that propelled modern AI. We introduce ARK, an open-source, Python-first robotics framework designed to close that gap. ARK presents a Gym-style environment interface that allows users to collect data, preprocess it, and train policies using state-of-the-art imitation-learning algorithms (e.g., ACT, Diffusion Policy) while seamlessly toggling between high-fidelity simulation and physical robots. A lightweight client-server architecture provides networked publisher-subscriber communication, and optional C/C++ bindings ensure real-time performance when needed. ARK ships with reusable modules for control, SLAM, motion planning, system identification, and visualization, along with native ROS interoperability. Comprehensive documentation and case studies-from manipulation to mobile navigation-demonstrate rapid prototyping, effortless hardware swapping, and end-to-end pipelines that rival the convenience of mainstream machine-learning workflows. By unifying robotics and AI practices under a common Python umbrella, ARK lowers entry barriers and accelerates research and commercial deployment of autonomous robots.
Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
Authors: Chenhao Li, Marco Hutter, Andreas Krause
Date: 2025-07-08 00:00:00
Tasks: Decision Making

This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional supervision that enables scalability and adaptation flexibility, but are prone to training instability and coarse reward signals. Recent advancements in both paradigms converge on the importance of structured motion representations, which enable smoother transitions, controllable synthesis, and improved task integration. We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced: rather than one paradigm dominating the other, the choice should be guided by task-specific priorities such as fidelity, diversity, interpretability, and adaptability. This work outlines the algorithmic trade-offs and design considerations that underlie method selection, offering a framework for principled decision-making in learning from demonstrations.
Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Authors: Chang Liu, Zhexiong Xue, Tamas Sziranyi
Date: 2025-07-08 00:00:00

Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficulty, A*, Dijkstra, RRT*, and a Novel Improved Ant Colony Optimization Algorithm (NIACO) are tested on the DeepGlobe satellite dataset. For 3D road-map path planning, 3D A*, 3D Dijkstra, RRT-Connect, and NIACO are evaluated using the Hamilton airborne LiDAR dataset, which provides detailed elevation information. All algorithms are assessed under identical start and end point conditions, focusing on path cost, computation time, and memory consumption. Results demonstrate that Dijkstra consistently offers the most stable and efficient performance in both 2D and 3D scenarios, particularly when operating on dense, pixel-level geospatial road-maps. These findings highlight the reliability of Dijkstra-based planning for static terrain navigation and establish a foundation for future research on dynamic path planning under complex environmental constraints.
Assessing Linear Control Strategies for Zero-Speed Fin Roll Damping
Authors: Nikita Savin, Elena Ambrosovskaya, Dmitry Romaev, Anton Proskurnikov
Date: 2025-07-08 00:00:00

Roll stabilization is a critical aspect of ship motion control, particularly for vessels operating in low-speed or zero-speed conditions, where traditional hydrodynamic fins lose their effectiveness. In this paper, we consider a roll damping system, developed by Navis JSC, based on two actively controlled zero-speed fins. Unlike conventional fin stabilizers, zero-speed fins employ a drag-based mechanism and active oscillations to generate stabilizing forces even when the vessel is stationary. We propose a simple linear control architecture that, however, accounts for nonlinear drag forces and actuator limitations. Simulation results on a high-fidelity vessel model used for HIL testing demonstrate the effectiveness of the proposed approach.
Video Event Reasoning and Prediction by Fusing World Knowledge from LLMs with Vision Foundation Models
Authors: L'ea Dubois, Klaus Schmidt, Chengyu Wang, Ji-Hoon Park, Lin Wang, Santiago Munoz
Date: 2025-07-08 00:00:00
Tasks: Future prediction, Large Language Model, Video Understanding, World Knowledge, Zero-shot Generalization

Current video understanding models excel at recognizing "what" is happening but fall short in high-level cognitive tasks like causal reasoning and future prediction, a limitation rooted in their lack of commonsense world knowledge. To bridge this cognitive gap, we propose a novel framework that synergistically fuses a powerful Vision Foundation Model (VFM) for deep visual perception with a Large Language Model (LLM) serving as a knowledge-driven reasoning core. Our key technical innovation is a sophisticated fusion module, inspired by the Q-Former architecture, which distills complex spatiotemporal and object-centric visual features into a concise, language-aligned representation. This enables the LLM to effectively ground its inferential processes in direct visual evidence. The model is trained via a two-stage strategy, beginning with large-scale alignment pre-training on video-text data, followed by targeted instruction fine-tuning on a curated dataset designed to elicit advanced reasoning and prediction skills. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple challenging benchmarks. Notably, it exhibits remarkable zero-shot generalization to unseen reasoning tasks, and our in-depth ablation studies validate the critical contribution of each architectural component. This work pushes the boundary of machine perception from simple recognition towards genuine cognitive understanding, paving the way for more intelligent and capable AI systems in robotics, human-computer interaction, and beyond.
3DGS_LSR:Large_Scale Relocation for Autonomous Driving Based on 3D Gaussian Splatting
Authors: Haitao Lu, Haijier Chen, Haoze Liu, Shoujian Zhang, Bo Xu, Ziao Liu
Date: 2025-07-08 00:00:00
Tasks: 3DGS, Autonomous Driving, Autonomous Navigation

In autonomous robotic systems, precise localization is a prerequisite for safe navigation. However, in complex urban environments, GNSS positioning often suffers from signal occlusion and multipath effects, leading to unreliable absolute positioning. Traditional mapping approaches are constrained by storage requirements and computational inefficiency, limiting their applicability to resource-constrained robotic platforms. To address these challenges, we propose 3DGS-LSR: a large-scale relocalization framework leveraging 3D Gaussian Splatting (3DGS), enabling centimeter-level positioning using only a single monocular RGB image on the client side. We combine multi-sensor data to construct high-accuracy 3DGS maps in large outdoor scenes, while the robot-side localization requires just a standard camera input. Using SuperPoint and SuperGlue for feature extraction and matching, our core innovation is an iterative optimization strategy that refines localization results through step-by-step rendering, making it suitable for real-time autonomous navigation. Experimental validation on the KITTI dataset demonstrates our 3DGS-LSR achieves average positioning accuracies of 0.026m, 0.029m, and 0.081m in town roads, boulevard roads, and traffic-dense highways respectively, significantly outperforming other representative methods while requiring only monocular RGB input. This approach provides autonomous robots with reliable localization capabilities even in challenging urban environments where GNSS fails.
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Authors: Young Hun Kim, Seungyeon Kim, Yonghyeon LEE, Frank Chongwoo Park
Date: 2025-07-08 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Contrastive Learning, Instance Segmentation, Object, Object Reconstruction, Semantic Segmentation

Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world settings where full-view or reliable depth data are often unavailable. Existing methods, whether based on strong symmetry priors or supervised learning on curated datasets, fail to generalize to such scenarios. In this work, we introduce DreamGrasp, a framework that leverages the imagination capability of large-scale pre-trained image generative models to infer the unobserved parts of a scene. By combining coarse 3D reconstruction, instance segmentation via contrastive learning, and text-guided instance-wise refinement, DreamGrasp circumvents limitations of prior methods and enables robust 3D reconstruction in complex, multi-object environments. Our experiments show that DreamGrasp not only recovers accurate object geometry but also supports downstream tasks like sequential decluttering and target retrieval with high success rates.
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Authors: Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Date: 2025-07-08 00:00:00
Tasks: MuJoCo, Recommendation Systems, Reinforcement Learning (RL)

Reward hacking in Reinforcement Learning (RL) systems poses a critical threat to the deployment of autonomous agents, where agents exploit flaws in reward functions to achieve high scores without fulfilling intended objectives. Despite growing awareness of this problem, systematic detection and mitigation approaches remain limited. This paper presents a large-scale empirical study of reward hacking across diverse RL environments and algorithms. We analyze 15,247 training episodes across 15 RL environments (Atari, MuJoCo, custom domains) and 5 algorithms (PPO, SAC, DQN, A3C, Rainbow), implementing automated detection algorithms for six categories of reward hacking: specification gaming, reward tampering, proxy optimization, objective misalignment, exploitation patterns, and wireheading. Our detection framework achieves 78.4% precision and 81.7% recall across environments, with computational overhead under 5%. Through controlled experiments varying reward function properties, we demonstrate that reward density and alignment with true objectives significantly impact hacking frequency ($p < 0.001$, Cohen's $d = 1.24$). We validate our approach through three simulated application studies representing recommendation systems, competitive gaming, and robotic control scenarios. Our mitigation techniques reduce hacking frequency by up to 54.6% in controlled scenarios, though we find these trade-offs are more challenging in practice due to concept drift, false positive costs, and adversarial adaptation. All detection algorithms, datasets, and experimental protocols are publicly available to support reproducible research in RL safety.
Towards Solar Altitude Guided Scene Illumination
Authors: Samed Doğan, Maximilian Hoh, Nico Leuze, Nicolas R. -Peña, Alfred Schöttl
Date: 2025-07-08 00:00:00
Tasks: Autonomous Driving

The development of safe and robust autonomous driving functions is heavily dependent on large-scale, high-quality sensor data. However, real-word data acquisition demands intensive human labor and is strongly limited by factors such as labeling cost, driver safety protocols and diverse scenario coverage. Thus, multiple lines of work focus on the conditional generation of synthetic camera sensor data. We identify a significant gap in research regarding daytime variation, presumably caused by the scarcity of available labels. Consequently, we present the solar altitude as global conditioning variable. It is readily computable from latitude-longitude coordinates and local time, eliminating the need for extensive manual labeling. Our work is complemented by a tailored normalization approach, targeting the sensitivity of daylight towards small numeric changes in altitude. We demonstrate its ability to accurately capture lighting characteristics and illumination-dependent image noise in the context of diffusion models.
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
Authors: Yegyu Han, Taegyoon Yoon, Dayeon Woo, Sojeong Kim, Hyung-Sin Kim
Date: 2025-07-08 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Benchmarking, Data Augmentation, Pose Estimation

Recent advances on 6D object-pose estimation has achieved high performance on representative benchmarks such as LM-O, YCB-V, and T-Less. However, these datasets were captured under fixed illumination and camera settings, leaving the impact of real-world variations in illumination, exposure, gain or depth-sensor mode - and the potential of test-time sensor control to mitigate such variations - largely unexplored. To bridge this gap, we introduce SenseShift6D, the first RGB-D dataset that physically sweeps 13 RGB exposures, 9 RGB gains, auto-exposure, 4 depth-capture modes, and 5 illumination levels. For three common household objects (spray, pringles, and tincase), we acquire 101.9k RGB and 10k depth images, which can provide 1,380 unique sensor-lighting permutations per object pose. Experiments with state-of-the-art models on our dataset show that applying sensor control during test-time induces greater performance improvement over digital data augmentation, achieving performance comparable to or better than costly increases in real-world training data quantity and diversity. Adapting either RGB or depth sensors individually is effective, while jointly adapting multimodal RGB-D configurations yields even greater improvements. SenseShift6D extends the 6D-pose evaluation paradigm from data-centered to sensor-aware robustness, laying a foundation for adaptive, self-tuning perception systems capable of operating robustly in uncertain real-world environments. Our dataset is available at: huggingface.co/datasets/Yegyu/SenseShift6D Associated scripts can be found at: github.com/yegyu-han/SenseShift6D
LiON-LoRA: Rethinking LoRA Fusion to Unify Controllable Spatial and Temporal Generation for Video Diffusion
Authors: Yisu Zhang, Chenjie Cao, Chaohui Yu, Jianke Zhu
Date: 2025-07-08 00:00:00

Video Diffusion Models (VDMs) have demonstrated remarkable capabilities in synthesizing realistic videos by learning from large-scale data. Although vanilla Low-Rank Adaptation (LoRA) can learn specific spatial or temporal movement to driven VDMs with constrained data, achieving precise control over both camera trajectories and object motion remains challenging due to the unstable fusion and non-linear scalability. To address these issues, we propose LiON-LoRA, a novel framework that rethinks LoRA fusion through three core principles: Linear scalability, Orthogonality, and Norm consistency. First, we analyze the orthogonality of LoRA features in shallow VDM layers, enabling decoupled low-level controllability. Second, norm consistency is enforced across layers to stabilize fusion during complex camera motion combinations. Third, a controllable token is integrated into the diffusion transformer (DiT) to linearly adjust motion amplitudes for both cameras and objects with a modified self-attention mechanism to ensure decoupled control. Additionally, we extend LiON-LoRA to temporal generation by leveraging static-camera videos, unifying spatial and temporal controllability. Experiments demonstrate that LiON-LoRA outperforms state-of-the-art methods in trajectory control accuracy and motion strength adjustment, achieving superior generalization with minimal training data. Project Page: https://fuchengsu.github.io/lionlora.github.io/