Embodied AI

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Embodied AI asks how agents can connect perception and reasoning to actions that change a physical or simulated world. Common approaches include vision-language-action models, language-model agents with tools and hierarchical plans, reinforcement learning, persistent spatial or episodic memory, and world models trained with robot demonstrations or generated simulation data. Researchers evaluate these systems on navigation, manipulation, household tasks, and multi-agent coordination, using benchmarks as well as real robots. A central challenge is that strong perception or short-task scores do not guarantee reliable long-horizon behavior: agents still struggle with physical constraints, changing conditions, recovery, and safety, while simulated success may not transfer to deployment.

Latest papers

257 papers28 papers in the last four weeks, down 15% on the four weeks before. 0.2% of all new papers.

EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents
Feb 26, 2026Wenjia Wang, Liang Pan, Huaijin Pi +83D Human Reconstruction Embodied AI
The University of Hong Kong · Tampere University · The Chinese University of Hong Kong +1
CWM: Contrastive World Models for Action Feasibility Learning in Embodied Agent Pipelines
Feb 25, 2026Chayan BanerjeeEmbodied AI Contrastive Learning
School of Electrical Engineering and Robotics, Queensland University of Technology (QUT).
Body-Reservoir Governance in Repeated Games: Embodied Decision-Making, Dynamic Sentinel Adaptation, and Complexity-Regularized Optimization
Feb 24, 2026Yuki NakamuraRepeated Games Game Theory
The Open University of Japan
How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
Feb 24, 2026Bo Peng, Pi Bu, Keyu Pan +7Embodied Agent Benchmarks Embodied AI
University of Science and Technology of China · Alibaba Group · Independent Researcher +1
PEPA: a Persistently Autonomous Embodied Agent with Personalities
Feb 21, 2026Kaige Liu, Yang Li, Lijun Zhu +1Cognitive Architectures for AI Agents Embodied AI
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China, and also with Shanghai Innovation Institute, Shanghai 200231, China · School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China, and also with the State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China +1
What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?
Feb 19, 2026Boyang Ma, Hechuan Guo, Peizhuo Lv +5Embodied AI AI Safety
Shandong University, 72 Binhai Road, Jimo, Qingdao, Shandong, 250100, China
HIMM: Human-Inspired Long-Term Memory Modeling for Embodied Exploration and Question Answering
Feb 17, 2026Ji Li, Bo Wang, Jing Xia +2Long-Term Memory for Embodied Agents Memory-Augmented Agents
The University of Hong Kong, Hong Kong SAR. · Beijing Institute of Technology, Beijing, China.
DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI
Feb 16, 2026En Yu, Haoran Lv, Jianjian Sun +46Flow Matching for Action Generation Vision-Language-Action Models
ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction
Feb 15, 2026Zeyi Zhang, Zixi Kang, Ruijie Zhao +4Human Motion Generation Human-Robot Interaction
Peking University, China
INHerit-SG: Incremental Hierarchical Semantic Scene Graphs for Complex Embodied Retrieval
Feb 13, 2026YukTungSamuel Fang, Zhikang Shi, Jiabin Qiu +5Graph-Based Retrieval 3D SGG
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Beihang University, Beijing, China
LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI
Jan 20, 2026Jianan Wang, Siyang Zhang, Bin Li +4Robotics Simulation AI Agent Evaluation
College of Computer Science and Technology, National University of Defense Technology · Intelligent Game and Decision Lab (IGDL), Beijing · School of Artifical Intelligence, Shanghai Jiao Tong University
Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents
Nov 24, 2025Dayong Liu, Chao Xu, Weihong Chen +5AI Agent Evaluation Multimodal Large Language Models
Zhejiang University · IROOTECH TECHNOLOGY · Wolf 1069 b Lab, Sany Group +2
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Oct 20, 2025Yulin Luo, Chun-Kai Fan, Menghang Dong +19Long-Horizon Robotic Manipulation Multimodal Large Language Models
State Key Laboratory of Multimedia Information Processing, School of Computer2026 Science, Peking University · Institute for Brain and Intelligence, Fudan University · University of Science andJul Technology Beijing +2
BuilderBench: The Building Blocks of Intelligent Agents
Oct 7, 2025Raj Ghugare, Roger Creus Castanyer, Catherine Ji +4Long-Horizon Agent Evaluation Robot Manipulation Benchmarks
Princeton University · Mila – Quebec AI Institute · Université de Montréal
$\mathcal{P}^3$ : Toward Versatile Embodied Agents
Aug 9, 2025Shengli Zhou, Xiangchen Wang, Jinrui Zhang +4Robot Task Planning Embodied AI
Southern University of Science and Technology · Spatialtemporal AI · MBZUAI
Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Jun 13, 2025Junha Lee, Eunha Park, Chunghyun Park +2Synthetic Data Generation for Robotics Embodied AI
Pohang University of Science and Engineering (POSTECH) · SqueezeBits · RLWLRD
Gibson Env: Real-World Perception for Embodied Agents
Aug 31, 2018Fei Xia, Amir Zamir, Zhi-Yang He +3Embodied AI Robotics Simulation
Stanford University · University of California, Berkeley

Feb 26, 2026J/K move · Enter open · S save

EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents

Wenjia Wang, Liang Pan, Huaijin Pi, Yuke Lou+7

The University of Hong Kong · Tampere University · The Chinese University of Hong Kong+1

3D Human Reconstruction Embodied AI Robotic Data Collection Dynamic Scene Reconstruction Multi-View 3D Reconstruction Humanoid Motion Tracking

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Human behaviors in the real world naturally encode rich, long-term contextual information that can be leveraged to train embodied agents for perception, understanding, and acting. However, existing capture systems typically rely on costly studio setups and wearable devices, limiting the large-scale collection of scene-conditioned human motion data in the wild. To address this, we propose EmbodMocap, a portable and affordable data collection pipeline using two moving iPhones. Our key idea is to jointly calibrate dual RGB-D sequences to reconstruct both humans and scenes within a unified metric world coordinate frame. The proposed method allows metric-scale and scene-consistent capture in everyday environments without static cameras or markers, bridging human motion and scene geometry seamlessly. Compared with optical capture ground truth, we demonstrate that the dual-view setting exhibits a remarkable ability to mitigate depth ambiguity, achieving superior alignment and reconstruction performance over single iphone or monocular models. Based on the collected data, we empower three embodied AI tasks: monocular human-scene-reconstruction, where we fine-tune on feedforward models that output metric-scale, world-space aligned humans and scenes; physics-based character animation, where we prove our data could be used to scale human-object interaction skills and scene-aware motion tracking; and robot motion control, where we train a humanoid robot via sim-to-real RL to replicate human motions depicted in videos. Experimental results validate the effectiveness of our pipeline and its contributions towards advancing embodied AI research.