Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, state discretization limits a robot's ability to follow human instructions based on the grounding of actions and states. To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve understanding object states and learning policies for continuous goals. To promote language-instructed learning, we provide expert demonstrations with template-generated language descriptions. We assess task performance by utilizing the latest language-conditioned policy learning models. Our results indicate that current models for language-conditioned manipulations continue to experience significant challenges in novel goal-state generalizations, scene generalizations, and object generalizations. These findings highlight the need to develop new algorithms that address this gap and underscore the potential for further research in this area. Project website: https://arnold-benchmark.github.io.
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能全国重点实验室）
SCLARO: A Dataset for Grounded Scenario-Level Scene Understanding and ScenarioCLIP for Benchmarking
Machine Intelligence Group, Department of CS&IS, Birla Institute of Technology and Science, Pilani – Hyderabad Campus（人工智能组，计算机科学与信息系，比拉理工学院和科学学院，比拉-海得拉巴校区）
MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Zhiyuan College, Shanghai Jiao Tong University（上海交通大学紫阳学院）；CPII under InnoHK（创新香港下的CPII）
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
SK T-Brain；University of California, Berkeley（加州大学伯克利分校）；Facebook AI Research（脸书人工智能研究院）；Seoul National University（首尔国立大学）；Georgia Institute of Technology（佐治亚理工学院）
Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（转化神经影像与数据科学三机构中心）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery
Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA（生物系统工程系，威斯康星大学麦迪逊分校）；Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA（农学系，威斯康星大学麦迪逊分校）
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
University of Bath（巴斯大学）；Zhejiang University（浙江大学）
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Technische Universität Darmstadt（达姆施塔特工业大学）；Center for Mind, Brain and Behavior, Uni. Marburg and JLU Giessen（马尔堡大学与吉森尤斯图斯·李比希大学心智、大脑与行为研究中心）；Amazon Alexa（亚马逊Alexa）
GrapeSplat: Geometry-Grounded Reconstruction via Amalgamated Pose-Free Encoding for Feed-Forward 3D Gaussian Splatting
National Taiwan University（国立台湾大学）；National Taiwan University of Science and Technology（国立台湾科技大学）；NTU AI Center of Research Excellence (NTU AI-CoRE)（台湾大学人工智能卓越研究中心）；VinUniversity（Vin大学）
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore（高性能计算研究所，科技研究局，新加坡）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Singapore Polytechnic（新加坡理工学院）
ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning
ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）；University of British Columbia（不列颠哥伦比亚大学）；University of California, Berkeley（加州大学伯克利分校）；Google（谷歌）；Microsoft（微软）
Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs
University of Turku（图尔库大学）；Centre for Artificial Intelligence, Zürich University of Applied Sciences（苏黎世应用科学大学人工智能中心）；Instituto Tecnológico de Aragón (ITA)（阿拉贡技术研究所）；University of Zaragoza（萨拉戈萨大学）
GuardianBench: A Same-Scene Instruction-Contrastive Benchmark for Latent Contextual Risk in Embodied AI
Shandong University（山东大学）；National University of Singapore（新加坡国立大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Xiaomi Corporation（小米公司）
FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·扎耶德人工智能大学）；Technical University of Munich（慕尼黑技术大学）；National University of Singapore（新加坡国立大学）；Westlake University（西湖大学）
