The paper introduces a multi‑modal late‑fusion perception pipeline for object detection and tracking in autonomous racing. It combines independent detections from cameras, LiDARs, and RADARs to produce timely and robust state estimates of surrounding vehicles. The tracking framework compensates for detection delays and incorporates vehicle dynamics and track layout knowledge, and its effectiveness is confirmed through real‑world experiments in diverse critical scenarios.
By Davide Malvezzi, Michele Pestarino, Vittoria Cavicchioli, Valentina La Gamba, Silvia Severi, Fabio Bagni, Luca Bartoli, Massimiliano Bosi, Francesco Gatti, Micaela Verucchi, Ayoub Raji, Marko Bertogna
arXiv:2609.10377v1 Announce Type: cross
Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities...
By Yuanxin Tian, Zhiyuan Liu, Jinhao Li, Zhenhua Xu, Wenhao Yu, Jianqiang Wang
PV-WM is a history‑only world model that jointly predicts pedestrian root motion, 15‑joint articulation, and vehicle kinematic states in a synchronized heterogeneous state. It uses recurrent updates to generate pedestrian and vehicle motion chunks, reconstructing vehicle boxes from predicted center, heading, and observed extent, and recomputes pedestrian‑vehicle geometry after each transition. Compared to a one‑shot predictor, PV‑WM reduces Root ADE by 12.7% and MPJPE by 14.8%, and across 824 Waymo contexts it lowers Root ADE by 5.2%, MPJPE by 7.6%, P‑V distance error by 11.9%, and oriented‑box closest‑approach error by 5.8%, while using 57.1% fewer parameters, 96.5% fewer FLOPs, and 25.5% lower p95 latency.
By Haozhuang Chi, Jingsong Liang, Ziying Song, Lei Yang, Shihao Li, Haoruo Zhang, Chen Lv
ComplicitSplat is a novel black‑box attack that leverages 3D Gaussian Splatting (3DGS) shading to create viewpoint‑specific camouflage, embedding adversarial content into scene objects that is only visible from certain angles. The method does not require access to model architecture or weights and can successfully fool a range of popular object detectors—including single‑stage, multi‑stage, and transformer‑based models—on both real‑world physical objects and synthetic scenes. This demonstrates that downstream models using 3DGS are vulnerable to adversarial manipulation.
By Matthew Hull, Haoyang Yang, Pratham Mehta, Mansi Phute, Aeree Cho, Haorang Wang, Matthew Lau, Wenke Lee, Wilian Lunardi, Martin Andreoni, Duen Horng Chau
arXiv:2609.09945v1 Announce Type: cross
Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
By Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei, Marcos R. Machado
arXiv:2609.09881v1 Announce Type: new
Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
By Toomas Tahves, Mauro Bellone, Raivo Sell
arXiv:2609.05519v1 Announce Type: cross
Abstract: We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points...
By Debasmita Ghose, Oz Gitelson, Michal Lewkowicz, Jake Brawer, Marynel Vazquez, Brian Scassellati
arXiv:2601.22475v2 Announce Type: replace
Abstract: Building a generalist robot policy requires continuously integrating new skills while preserving previously acquired behaviors. Directly optimizing...
By Qijun He, Yuxuan Li, Mingqi Yuan, Xiaoquan Sun, Wen-Tse Chen, Jeff Schneider, Jiayu Chen
WildRelight is the first in-the-wild dataset designed to evaluate single-image relighting models, featuring high-resolution outdoor scenes captured under strictly aligned, temporally varying natural illuminations paired with high-dynamic-range environment maps. The benchmark demonstrates that state-of-the-art models trained on synthetic data suffer severe domain shifts when applied to real-world imagery. Leveraging the dataset’s temporal structure, the authors introduce a physics-guided inference framework combining Diffusion Posterior Sampling with Temporal Sampling-Aware Test-Time Adaptation, enabling synthetic models to self-supervise and align with real-world statistics on-the-fly.
By Lezhong Wang, Mehmet Onurcan Kaya, Siavash Bigdeli, Jeppe Revall Frisvad
arXiv:2609.08084v1 Announce Type: cross
Abstract: Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computati...
By Igor Pavlovic, Thiemo Wandel, Anton Obukhov, Luca Bartolomei, Andrey Davydov, Fabio Tosi, Matteo Poggi, Sabine S\"usstrunk, Dengxin Dai
arXiv:2609.06880v1 Announce Type: cross
Abstract: Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated pers...
By Mimo Shirasaka, Haochen Zhang, Yonatan Bisk
BinauralVAE is an open‑source pipeline that reconstructs spatial audio using various Variational Autoencoder architectures, including complex‑valued variants, to learn latent representations of binaural signals. The project builds on realistic acoustic data from a simulated robot navigating an environment, providing a foundation for audio‑centric world models. It aims to map the causal link between navigational actions and their acoustic outcomes, positioning sound as a complementary modality for spatial awareness.
By Luis Vitor Zerkowski, Luiz Velho
arXiv:2609.05516v1 Announce Type: cross
Abstract: Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single...
By Zhiyuan Nie, Zixi Zhou, Xianbin Gu
AtomicVLA is a unified planning-and-execution framework that generates task-level plans, atomic skill abstractions, and fine-grained actions for robotic manipulation. It builds a scalable atomic skill library using a Skill‑Guided Mixture‑of‑Experts (SG‑MoE) and a flexible routing encoder that assigns new skills to dedicated experts, enabling continual learning. Experiments show that AtomicVLA outperforms baseline models on both simulated and real‑world long‑horizon tasks, achieving significant improvements in task performance and learning efficiency.
By Likui Zhang, Tao Tang, Zhihao Zhan, Xiuwei Chen, Zisheng Chen, Jianhua Han, Jiangtong Zhu, Pei Xu, Hang Xu, Hefeng Wu, Liang Lin, Xiaodan Liang
arXiv:2609.10082v1 Announce Type: cross
Abstract: Accurate camera intrinsic calibration is fundamental to robot perception, and the accuracy depends on the quality of the collected images. However, e...
By Xiangcheng Hu
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and ro...
Accurate contact prediction is useful for robotic manipulation only if it supports effective decisions. We investigate this connection using a compact, randomly initialized visuotactile world model, t...
Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edg...
SyncWorld is an action‑conditioned world model that functions as a zero‑shot simulator across unseen environments without additional training. It uses a visual calibration episode—paired frames and actions that expose all controllable degrees of freedom—to define a setup‑specific Action‑Visual Mapping in context. By training with these calibration contexts, the model learns to interpret actions through visual evidence and to leverage interaction history when explicit calibration is unavailable, enabling accurate simulation of action outcomes and test‑time policy improvement.
The paper presents a four‑module, data‑driven framework to identify and prioritize robotic process automation (RPA) opportunities in U.S. hospitals. It includes a process taxonomy, an automation suitability index, a tool‑tier selection recommendation, and a return‑on‑investment analysis, all applied to a synthetic portfolio of twenty hospital processes. The authors demonstrate the framework’s robustness through Monte Carlo simulations and discuss governance and future validation steps.