The paper presents a decoupled framework for sim-to-real traffic scene understanding, separating semantic fact extraction from caption generation. It uses a frozen V-JEPA encoder for predictive scene representations and a lightweight Llama-based predictor for VQA, followed by a training-free structured refinement that leverages statistical priors, inter-question relationships, and temporal consistency. The refined facts are then fed to Qwen3-VL-8B to produce pedestrian and vehicle descriptions, achieving top performance on the 2026 AI City Challenge Track 2 benchmark with 87.09% VQA accuracy and an overall S2 score of 60.0853.
By Nguyen Hoai Thuong Bui, Thanh Nguyen Vo, Trinh Tra Giang Nguyen, Ha Duc Bui
The paper introduces Adaptive Agents via Latent Topologies (AALT), a method for active imitation learning that selects demonstrations based on their expected impact on start‑to‑goal connectivity rather than generic information gain. AALT builds a latent topology of hub states and learned behaviors, identifies high‑value bridge demonstrations that can solve many tasks simultaneously, and uses these to condition a diffusion policy for planning. In a simulated UR5e robot retrieval task with 72 start‑goal pairs, AALT achieved 100% success after only three demonstrations, outperforming baselines that required many more queries.
By Maxwell J. Jacobson, Ahmed H Qureshi, Yexiang Xue
The paper introduces ARC, a hubs‑based framework that trains undergraduate mentors at colleges to run workshops for nearby K‑12 robotics teams, allowing mature programs to become secondary hubs and expand mentorship reach. A trial at one university created three rural teams, showing significant gains in students’ programming knowledge, resource access, and practice opportunities, as well as increased confidence among undergraduate mentors. A spatial Markov model predicts that, under moderate conditions, ARC could reach 74% of Indiana’s public K‑12 schools and generate 992 robotics programs in 40 years, far exceeding natural growth projections.
By Maxwell J. Jacobson, Gustavo Rodriguez-Rivera, Petros Drineas, Yexiang Xue
SurgRAW introduces a multi‑agent, chain‑of‑thought workflow for robotic surgical video analysis, leveraging a new SurgCoTBench benchmark with 14,256 QA pairs across five surgical tasks. The system uses an orchestrator to split scene understanding into two reasoning streams, panel‑discussion‑style collaboration among task‑specific agents, and retrieval‑augmented generation to incorporate surgical knowledge. Experiments show SurgRAW outperforms mainstream vision‑language models and a supervised baseline by 14.61% accuracy.
By Chang Han Low, Ziyue Wang, Tianyi Zhang, Zhu Zhuo, Zhitao Zeng, Evangelos B. Mazomenos, Yueming Jin
The paper introduces ASTRIL-MPC, a language‑guided neural model predictive control framework that enables articulated tracked robots to navigate complex, contact‑rich urban environments such as stairwells and cluttered interiors. By combining a learned kinematics model that predicts short‑horizon state changes, an optimization‑based planner with multi‑objective costs, and a large language model that safely updates control weights, the system achieves up to 71% better traversal quality than non‑adaptive NMPC and 67% better than a PPO baseline, while eliminating collision impacts during descent. Real‑robot trials over four indoor obstacles confirm the method’s transferability to physical contact‑rich traversal.
By Zhenfeng Gan, Yanbo Chen, Lirong Che, Yongyi Ma, Rongkai Zhu, Xueqian Wang
CueNav is a video model-based navigation framework that uses visual cues—a Bird's-Eye View map for global task context and a body-aware egocentric view for embodiment context—to guide a video planner. The framework couples this planner with an embodiment-specific Inverse-Dynamics Model that translates dense flow fields from the video plan into robot actions. Experiments show that CueNav nearly doubles maze navigation success compared to cue-less planning and achieves 70% success in narrow passages, while also supporting zero-shot semantic-conditioned navigation across different robot platforms.
By Hojin Lee, Sizhe Lester Li, Maximilian Hilger, Susie Lu, Achim J. Lilienthal, Vincent Sitzmann, Daniel A. Duecker
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
By Ranjan Sapkota, Manoj Karkee
RoboVAD is a large-scale benchmark for video anomaly detection in robotic arm manipulation, featuring cross-domain evaluation where both tasks and anomaly types are unseen during training. The dataset challenges existing VAD methods, with state-of-the-art approaches, including a new method tailored for robotic arms, still achieving less than 70% micro-averaged frame-level AUC in the hardest setting. The authors provide the dataset and code publicly for further research.
By Alexandru-Bogdan Dura, Sebastian Balmus, Radu Tudor Ionescu
The paper introduces LDE, a framework that uses collaborative perception (CP) to generate high‑quality pseudo‑labels for unsupervised model adaptation in autonomous driving. It tackles communication limits, field‑of‑view mismatches, and label unreliability through selective feature sharing, FoV filtering, and curriculum learning. Experiments on 3D object detection show LDE surpasses pre‑trained models and existing adaptation methods.
By Yanan Ma, Yihang Tao, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
The paper investigates how to balance retaining past experience versus learning from new data when robot dynamics change. It introduces two metrics—change magnitude and age‑staleness AUC—to quantify when older transitions are helpful or harmful. Experiments on locomotion tasks and real‑world perturbations show that the optimal replay strategy depends on the size of the dynamics shift and the evolution of the system over time.
By Everest Yang, Skye Thompson, George D. Konidaris
The paper presents Real‑Time EXPO‑FT, a reinforcement learning framework that fine‑tunes large Vision‑Language‑Action models for real‑time robotic control. It separates slow, expressive action generation from fast, reactive edits, allowing a lightweight policy to adjust actions based on the latest observation. Experiments on the Kinetix benchmark and four dynamic real‑world tasks show that Real‑Time EXPO‑FT achieves superior performance, improving policy success rates from 42% to 97% with only ten minutes of online data and no human intervention.
By Perry Dong, Kuo-Han Hung, Dorsa Sadigh, Chelsea Finn
The paper introduces a metric interaction framework for robotic manipulation that explicitly models object- and scene-level interactions in Cartesian space. It uses Interaction‑Centric Tokens (ICTs) to represent end‑effector trajectories relative to objects and a Metric Action Interaction Field (MAIF) to attend to scene point‑cloud features for geometry‑conditioned action corrections. Experiments show modest but consistent improvements across several benchmarks, including LIBERO, RoboTwin 2.0, and real‑world tasks.
By Lijie Wang, Zheng Lu, Yiming Wang, Heyang Yu, Kenghou Hoi, Bowen Hu, Di Cui, Tianyu Xin, Haoran Liao, Wanqi Zhong, Xingjie Fan, Yizhao Xu, Ziliang Wang, Fei Gao, Yiming Li
arXiv:2509.12151v3 Announce Type: replace-cross
Abstract: We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulat...
By Zongyao Yi, Joachim Hertzberg, Martin Atzmueller
SynAgent is a framework that uses large language model agents to run autonomous experiments while building an explicit, revisable understanding of the synthesis process. Unlike traditional black‑box optimizers, SynAgent generates analysis skills on the fly and reasons multimodally over data such as X‑ray diffraction patterns and electron micrographs. In an 18‑experiment campaign on LiCoO₂ thin‑film deposition, SynAgent produced highly crystalline films and uncovered a sharp temperature threshold and optimal growth window (650–690 °C) for crystallization.
By Izumi Takahara, Kazunori Nishio, Akira Aiba, Shigeru Kobayashi, Takao Nakajima, Taro Hitosugi, Teruyasu Mizoguchi
PACT‑WAM is a world‑action model that simultaneously predicts a 16‑step action trajectory and its corresponding visual forecast for robot manipulation. It uses a hierarchical history encoder that compresses past observations into fewer tokens, reducing processing cost by 75% compared to dense encoding. The model’s shared flow module updates action and visual states jointly, and a TiTok‑VAE decoder reconstructs multi‑view future images, which are then used by a vision‑language component (Proposal Review) to improve execution‑prefix selection and proposal rejection, boosting success rates on several benchmarks.
By Yushan Liu, Jingjing Fan, Shoujie Li, Yifan Xie, Xiao-Ping Zhang, Wenbo Ding
AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
By Jiabin Lou, Yirong Yang, Haopeng Wang, Xuxin Lv, Xinyu Liu, Diyuan Hou, Xuehong Liu, Rongye Shi, Wenjun Wu
PhysVGGT is a feed‑forward model that predicts dense maps of friction coefficient, Shore hardness, Young's modulus, and density, along with object‑level mass, from a single RGB image in one forward pass. It treats physical property estimation as a dense per‑pixel prediction problem, using a visual geometry transformer to extract geometry‑aware tokens and separate dense and global prediction branches. A scalable pseudo‑label generation pipeline enables large‑scale weakly supervised training, and the model achieves state‑of‑the‑art performance on the ABO‑500 dataset while running 27× faster than previous methods.
By Sneha Paul, Guile Wu, Bingbing Liu, Dongfeng Bai
LiteViLNet is a lightweight RGB‑geometry fusion network for road segmentation that uses a MobileNetV3 RGB encoder and a tiny depth‑wise‑separable geometry encoder. Its multi‑scale fusion module enhances modality‑specific features, performs cross‑modal interaction, and applies adaptive gating, while a depth‑wise large‑kernel bridge expands contextual support with minimal overhead. The U‑Net‑style decoder is trained with deep supervision, achieving state‑of‑the‑art performance on KITTI and ORFD benchmarks and running at up to 68.73 FPS on a Jetson Orin NX with TensorRT FP16.
By Daojie Peng, Bingtao Wang, Fulong Ma, Liang Zhang, Jun Ma
arXiv:2609.18058v1 Announce Type: new
Abstract: Finding the object referred to by language in a partially observed 3D scene is a core capability for embodied agents. Existing approaches either couple...
By Shixiong Xu, Zhiyuan Chen, Song Ding, Rui Luo, Xiaowei Liang, Dongxu Miao, Zhiying Du