The Year in AI Papers
The year/Topics/Robotics and embodied AI

Topic area

Robotics and embodied AI

Every collection across robotics and embodied ai.

Papers
59
Research labs
2
Official code
45

1–50 of 59 papers in this topic area

01

Independent research

PhiZero: A World Model Built Around Physical Language

PHIZERO is a physical world model that learns a compact discrete 'physical language' from in-the-wild videos via self-supervision, representing state transitions. It adopts a reason-then-render paradigm: a Physical Language Reasoner (initialized from Qwen3-VL-4B) predicts a physical-language sequence from the first frame and a textual action intent, and a…

Shuyao Shang, Yuqi Wang, Ruopeng Gao, Xu Chen, et al.
Published
Jul 2026
Citations
0
Code
77 stars
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02

Independent research

TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

TurboVLA introduces a new paradigm for vision-language-action (VLA) models, replacing the conventional LLM-centric V→L→A pathway with a direct V+L→A mapping. It uses a lightweight BERT text encoder and a DINOv3 vision encoder, with bidirectional cross-attention to fuse visual and linguistic features, and an ACT-style decoder to predict continuous action…

Hengyi Xie, Chenfei Yao, Xianjin Wu, Xuanyang Xi, et al.
Published
Jul 2026
Citations
0
Code
385 stars
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03

Independent research

HumanCLAW: Can Vision-Language Models Act Through a Body?

HumanCLAW is an evaluation framework that measures whether vision-language models (VLMs) can act through a physical body by decoupling action decision-making from low-level motor execution. A harnessed VLM issues atomic skill commands (e.g., walk, turn, sit) at sub-second intervals, which are converted into continuous full-body motion by a…

Siyao Li, Jiawei Gu, Shuai Liu, Kairui Hu, et al.
Published
Jul 2026
Citations
0
Code
58 stars
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04

Independent research

HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

HiFi-UMI is a portable data-production system that co-designs hardware and software to achieve high-fidelity robot-free UMI data, aiming to eliminate the need for real-robot teleoperation in post-training. It uses head-mounted offline stereo-inertial SLAM, native inter-gripper relative pose, microsecond GPIO synchronization, and ultra-wide 200°…

Simple AI, :, Yuteng Wei, Jinming Ma, et al.
Published
Jul 2026
Citations
0
Code
Not linked
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05

Independent research

$N_0$-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens

N0-VTLA is a vision-tactile-language-action (VTLA) foundation model for contact-rich manipulation. It introduces latent tactile tokens that predict future tactile changes over the action horizon, conditioning a flow-matching action expert, rather than using current tactile readings as observation context. The model is pretrained on NeoData, a large-scale…

NeoteAI Team, Fudan TEAI Team
Published
Jul 2026
Citations
1
Code
51 stars
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06

Independent research

Progress Reward Modeling for Robotic Learning: A Comprehensive Survey

This survey provides a unified framework for progress reward modeling in robotic learning, addressing the fragmentation in the field. It organizes the domain into three connected steps: the interface of progress models (input/output structure), the methods for constructing progress rewards, and the data and benchmarks used for evaluation. The interface…

Jianshu Zhang, Keliang Wu, Haoran Lu, Anbang Liu, et al.
Published
Jul 2026
Citations
0
Code
69 stars
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07

Independent research

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

RynnBrain 1.1 is a family of embodied foundation models (2B, 9B, 122B-A10B) built on Qwen3.5, introducing contact-point prediction and native 3D grounding (for 2B/9B) to better align with robot manipulation. The 122B-A10B model outperforms all evaluated proprietary and open-source models on VSI-Bench, MMSI, and RefSpatial-Bench. Scaling analysis shows…

Kehan Li, Bohan Hou, Minghao Zhu, Tianyi Zhang, et al.
Published
Jul 2026
Citations
2
Code
Not linked
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08

Independent research

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

Xiaomi-Robotics-1 is a foundational vision-language-action (VLA) model trained on over 100,000 hours of real-world manipulation trajectories collected via UMI devices. It uses a two-stage training recipe: pre-training on UMI data with auto-labeled state-transition language prompts (generated by a VLM) to learn generalizable action generation, and…

Xiaomi Robotics Team, Jun Guo, Piaopiao Jin, Jason Li, et al.
Published
Jul 2026
Citations
5
Code
454 stars
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09

Independent research

Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning

Open-AoE is an open, community-oriented egocentric manipulation dataset and toolchain from Ant Group, covering the full pipeline from smartphone capture to model training. Its first release includes approximately 2,000 hours of manipulation video collected in natural environments by 500+ contributors using 400+ smartphone models, spanning 400+ scenes and…

Zishuo Li, Bowen Yang, Changtao Miao, Kai Zhu, et al.
Published
Jul 2026
Citations
1
Code
135 stars
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10

Independent research

ABot-N1: Toward a General Visual Language Navigation Foundation Model

ABot-N1 is a general visual language navigation foundation model from AMAP CV Lab (Alibaba Group) that decouples cognition from control using a slow-fast architecture. The slow system (4B-parameter VLM) performs Chain-of-Thought reasoning and outputs pixel goals (affordance and target pixels) in image space, serving as a universal interface for five tasks:…

Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, et al.
Published
Jul 2026
Citations
0
Code
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11

Independent research

Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation

LaMem-VLA introduces a dual latent memory framework for Vision-Language-Action (VLA) models to address the temporal short-horizon bias in long-horizon robotic manipulation. Unlike existing memory-augmented VLAs that store history externally and consume it as auxiliary policy-side context, LaMem-VLA represents historical experience as context-native latent…

Hongyu Qu, Jianzhe Gao, Xiaobin Hu, Shaohuan Yang, et al.
Published
Jul 2026
Citations
1
Code
11 stars
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12

Independent research

RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation

RynnWorld-4D is a 4D embodied world model that generates synchronized RGB, depth, and optical flow (RGB-DF) videos from a single RGB-D image and a language instruction, using a tri-branch diffusion architecture with cross-modal attention and 3D RoPE. The authors curate Rynn4DDataset 1.0, containing over 254.4 million frames from human and robotic…

Haoyu Zhao, Xingyue Zhao, Siteng Huang, Xin Li, et al.
Published
Jul 2026
Citations
1
Code
79 stars
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13

Independent research

RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation

RynnWorld-Teleop introduces digital teleoperation, a paradigm that replaces physical robots with a generative world model for scalable robot data collection. An operator's hand-pose stream drives a robot-centric world model to synthesize high-fidelity egocentric videos from a single reference image, with the pose stream serving as an embodiment-agnostic…

Haoyu Zhao, Xingyue Zhao, Hangyu Li, Biao Gong, et al.
Published
Jul 2026
Citations
0
Code
Not linked
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14

Independent research

Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots

Embodied.cpp is a portable C++ inference runtime for embodied AI models, addressing the fragmented deployment of vision-language-action (VLA) models and world-action models (WAMs) on heterogeneous edge devices. It identifies three key runtime requirements: multi-rate execution, latency-first batch-1 inference, and extensible embodied interfaces. The…

Ling Xu, Chuyu Han, Borui Li, Hao Wu, et al.
Published
Jul 2026
Citations
1
Code
121 stars
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15

Independent research

In-Context World Modeling for Robotic Control

The paper introduces In-Context World Modeling (ICWM), a framework that enables Vision-Language-Action (VLA) models to adapt to novel system configurations (e.g., camera viewpoints, robot morphologies) without parameter updates. Standard VLA models condition only on current observations and language instructions, implicitly assuming a fixed execution…

Siyin Wang, Junhao Shi, Senyu Fei, Zhaoyang Fu, et al.
Published
Jun 2026
Citations
0
Code
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16

Independent research

World Action Models: A Survey

This survey defines World Action Models (WAMs) as embodied predictive-action models that make a forecast of the future available to action, distinguishing them from VLAs, world models, and video generation models. It organizes WAMs via two complementary views: a design-philosophy taxonomy (Render-and-Decode, Latent-Only, Video-Generation-Free) and a…

Qiuhong Shen, Shihua Zhang, Yue Liao, Qi Li, et al.
Published
Jun 2026
Citations
1
Code
308 stars
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17

Independent research

Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models

The paper introduces Act2Answer, a protocol for evaluating commonsense and world knowledge retention in Vision-Language-Action (VLA) models. It converts VLM knowledge benchmarks into embodied tabletop episodes where agents answer by placing a cube on one of two candidate images, reducing control confounds. The suite includes 1,720 binary questions across…

Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin, Nikita Kurlaev, et al.
Published
Jun 2026
Citations
0
Code
11 stars
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18

Independent research

ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining

ACE-EGO-0 is a unified Vision-Language-Action (VLA) pretraining framework that jointly trains on heterogeneous embodied data, including egocentric human videos, multi-embodiment robot demonstrations, and simulation rollouts. It addresses representation heterogeneity via a unified action representation: camera-space actions (spatial alignment),…

Hao Li, Ganlong Zhao, Yufei Liu, Haotian Hou, et al.
Published
Jun 2026
Citations
1
Code
37 stars
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19

Independent research

Geometric Action Model for Robot Policy Learning

The paper introduces the Geometric Action Model (GAM), a language-conditioned manipulation policy that repurposes a pretrained geometric foundation model (GFM) as a shared substrate for perception, temporal prediction, and action decoding. GAM splits the GFM at an intermediate layer: shallow layers encode observations, a causal future predictor inserted at…

Jisang Han, Seonghu Jeon, Jaewoo Jung, René Zurbrügg, et al.
Published
Jun 2026
Citations
1
Code
173 stars
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20

Independent research

DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects

DragMesh-2 is a contact-driven framework for dexterous hand interaction with articulated objects, where the target part moves only through physical hand–handle contact. It extends prior object-centric generation to hand-driven interaction, addressing the challenge that policies trained under fixed dynamics overfit nominal contact loads and degrade under…

Tianshan Zhang, Yijia Duan, Yanjun Li, Zeyu Zhang, et al.
Published
Jun 2026
Citations
0
Code
8 stars
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21

Independent research

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

LabVLA addresses the gap between AI-driven scientific reasoning and physical laboratory execution by introducing a Vision-Language-Action (VLA) model trained for scientific laboratory protocols. The authors identify data and embodiment as central bottlenecks, addressed via RoboGenesis, a simulation-based data engine that generates LabEmbodied-Data, a…

Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, et al.
Published
Jun 2026
Citations
0
Code
93 stars
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22

Independent research

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

Embodied-R1.5 is an 8B-parameter Embodied Foundation Model (EFM) that unifies three embodied reasoning dimensions—cognition and spatial reasoning, planning and correction, and pointing and location—within a single architecture. It uses three automated data pipelines to build a 15B-token corpus and a multi-task balanced RL recipe to resolve heterogeneous…

Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, et al.
Published
Jun 2026
Citations
1
Code
48 stars
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23

Qwen

Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Qwen-VLA is a unified embodied foundation model that extends the Qwen3.5-4B vision-language backbone with a DiT-based flow-matching action decoder, enabling a single model to handle manipulation, navigation, and trajectory prediction across diverse tasks, environments, and robot embodiments. It uses embodiment-aware prompt conditioning and a unified…

Qiuyue Wang, Mingsheng Li, Jian Guan, Jinhui Ye, et al.
Published
May 2026
Citations
20
Code
Not linked
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24

arXiv.org

DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo

DexJoCo is a benchmark and toolkit for task-oriented dexterous manipulation on MuJoCo, featuring 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning. It includes a low-cost teleoperation system using Rokoko gloves and HTC Vive trackers, with a retargeting module (GeoRT) to map human hand…

Hanwen Wang, Weizhi Zhao, Xiangyu Wang, Siyuan Huang, et al.
Published
May 2026
Citations
5
Code
171 stars
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25

arXiv.org

PhysBrain 1.0 Technical Report

PhysBrain 1.0 introduces a training strategy for embodied AI that prioritizes physical commonsense acquisition over pure action imitation. Its data engine converts large-scale human egocentric video (Ego4D, BuildAI, EgoDex, EPIC, SEA-Small) into structured scene meta-information—scene elements, spatial dynamics, action execution, and depth-aware…

Shijie Lian, Bin Yu, Xiaopeng Lin, Changti Wu, et al.
Published
May 2026
Citations
0
Code
37 stars
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26

arXiv.org

World Action Models: The Next Frontier in Embodied AI

This survey introduces World Action Models (WAMs), a new paradigm in embodied AI that unifies predictive world modeling with action generation, targeting a joint distribution over future states and actions. WAMs are formally defined and distinguished from related concepts like Video Policies and Action World Models. The paper organizes existing methods…

Siyin Wang, Junhao Shi, Zhaoyang Fu, Xinzhe He, et al.
Published
May 2026
Citations
29
Code
1.2K stars
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27

arXiv.org

RLDX-1 Technical Report

RLDX-1 is a general-purpose Vision-Language-Action model (VLA) for dexterous manipulation, integrating versatile intelligence with functional capabilities: motion awareness, long-term memory, and physical sensing. Built on the Multi-Stream Action Transformer (MSAT) and a Qwen3-VL 8B backbone, it processes heterogeneous modalities via dedicated streams with…

Dongyoung Kim, Huiwon Jang, Myungkyu Koo, Suhyeok Jang, et al.
Published
May 2026
Citations
11
Code
326 stars
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28

arXiv.org

MolmoAct2: Action Reasoning Models for Real-world Deployment

MolmoAct2 is a fully open Vision-Language-Action (VLA) model family for real-world robot deployment, built on the Molmo2-ER backbone, a VLM specialized for spatial and embodied reasoning trained on a 3.3M-sample corpus. It introduces three new robot datasets: MolmoAct2-BimanualYAM (720 hours, largest open bimanual dataset), MolmoAct2-DROID…

Haoquan Fang, Jiafei Duan, Donovan Clay, Sam Wang, et al.
Published
May 2026
Citations
23
Code
697 stars
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29

arXiv.org

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

This preprint introduces a taxonomy for world models in agentic AI, organized along two axes: capability level (L1 Predictor, L2 Simulator, L3 Evolver) and governing-law regime (physical, digital, social, scientific). L1 models learn one-step transition operators; L2 composes them into multi-step, action-conditioned rollouts satisfying long-horizon…

Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin, Lingdong Kong, et al.
Published
Apr 2026
Citations
10
Code
295 stars
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30

arXiv.org

OpenWorldLib: A Unified Codebase and Definition of Advanced World Models

OpenWorldLib is a standardized inference framework for advanced world models, addressing the lack of a unified definition in the field. The paper proposes that a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. It categorizes…

DataFlow Team, Bohan Zeng, Daili Hua, Kaixin Zhu, et al.
Published
Apr 2026
Citations
3
Code
853 stars
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31

arXiv.org

LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models

The paper introduces LIBERO-Para, a benchmark for evaluating paraphrase robustness in Vision-Language-Action (VLA) models, and PRIDE, a metric for fine-grained robustness analysis. LIBERO-Para varies action expressions and object references independently, yielding 43 paraphrase types. Across seven VLA configurations (0.6B–7.5B), consistent performance…

Chanyoung Kim, Minwoo Kim, Minseok Kang, Hyunwoo Kim, et al.
Published
Mar 2026
Citations
3
Code
42 stars
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32

arXiv.org

CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence

CARLA-Air is an open-source simulation platform that integrates CARLA and AirSim within a single Unreal Engine process, enabling joint simulation of aerial and ground agents for embodied intelligence research. It resolves the UE4 single-game-mode conflict by inheriting CARLA's ground subsystems and composing AirSim's flight actor as a world entity,…

Tianle Zeng, Yanci Wen, Hong Zhang
Published
Mar 2026
Citations
2
Code
1.1K stars
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33

arXiv.org

FASTER: Rethinking Real-Time Flow VLAs

FASTER is a method to reduce reaction latency in flow-based Vision-Language-Action (VLA) models for real-time robot control. It introduces a Horizon-Aware Schedule (HAS) that adaptively allocates more sampling steps to near-term actions, enabling the immediate action to be generated in a single step while preserving long-horizon trajectory quality. This…

Yuxiang Lu, Zhe Liu, Xianzhe Fan, Zhenya Yang, et al.
Published
Mar 2026
Citations
10
Code
Not linked
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34

arXiv.org

HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions

HSImul3R is a unified framework for reconstructing simulation-ready 3D human–scene interactions (HSI) from casual captures like sparse-view images and monocular videos. It addresses the perception–simulation gap where visually plausible reconstructions fail in physics engines. The method uses a physically-grounded bi-directional optimization pipeline with…

Yukang Cao, Haozhe Xie, Fangzhou Hong, Long Zhuo, et al.
Published
Mar 2026
Citations
2
Code
43 stars
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35

arXiv.org

GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning

GigaBrain-0.5M* is a vision-language-action (VLA) model trained via world model-based reinforcement learning, built on GigaBrain-0.5, which is pretrained on over 10,000 hours of robotic manipulation data and ranks first on the RoboChallenge benchmark. The model integrates RAMP (Reinforcement leArning via world Model-conditioned Policy), a four-stage…

GigaBrain Team, Boyuan Wang, Bohan Li, Chaojun Ni, et al.
Published
Feb 2026
Citations
7
Code
Not linked
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36

NVIDIA

PhyCritic: Multimodal Critic Models for Physical AI

PhyCritic is a multimodal critic model designed for physical AI tasks, addressing the lack of physics-aware evaluation in existing judge models. It uses a two-stage RLVR pipeline: first, a physical skill warmup stage with GRPO on physical QA pairs to enhance perception and reasoning; second, self-referential critic finetuning, where the critic generates…

Tianyi Xiong, Shihao Wang, Guilin Liu, Yi Dong, et al.
Published
Feb 2026
Citations
9
Code
Not linked
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37

arXiv.org

Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning

The paper introduces Recurrent-Depth VLA (RD-VLA), a Vision-Language-Action model that enables adaptive test-time compute via latent iterative reasoning. Unlike token-based Chain-of-Thought methods, RD-VLA uses a weight-tied recurrent action head that refines a latent scratchpad over multiple iterations, keeping memory constant. The architecture includes a…

Yalcin Tur, Jalal Naghiyev, Haoquan Fang, Wei-Chuan Tsai, et al.
Published
Feb 2026
Citations
11
Code
42 stars
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38

arXiv.org

RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI

USER is a unified and extensible system for real-world online policy learning in embodied AI, addressing the challenges of physical-world training such as lack of acceleration, reset, and replication. It treats robots as first-class hardware resources alongside GPUs via a unified hardware abstraction layer, enabling automatic discovery, management, and…

Hongzhi Zang, Shu'ang Yu, Hao Lin, Tianxing Zhou, et al.
Published
Feb 2026
Citations
4
Code
Not linked
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39

arXiv.org

Green-VLA: Staged Vision-Language-Action Model for Generalist Robots

Green-VLA is a staged Vision-Language-Action (VLA) framework for real-world robot deployment, developed by Sber Robotics Center. It uses a five-stage curriculum: L0 (base VLM), L1 (web pretraining), R0 (multi-embodiment robotics pretraining), R1 (embodiment-specific fine-tuning), and R2 (RL alignment). The framework unifies 24M web samples and 3,000 hours…

I. Apanasevich, M. Artemyev, R. Babakyan, P. Fedotova, et al.
Published
Jan 2026
Citations
4
Code
138 stars
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40

arXiv.org

DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation

DynamicVLA is a 0.4B-parameter Vision-Language-Action (VLA) model designed for dynamic object manipulation, addressing latency issues in existing VLAs. It introduces three key innovations: a compact architecture using a convolutional vision encoder (FastViT) and a truncated SmolLM2-360M backbone for fast inference; Continuous Inference, which overlaps…

Haozhe Xie, Beichen Wen, Jiarui Zheng, Zhaoxi Chen, et al.
Published
Jan 2026
Citations
24
Code
318 stars
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41

arXiv.org

LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries

LangForce is a framework for Vision-Language-Action (VLA) models that addresses the 'vision shortcut' pathology, where models ignore language instructions due to dataset bias. In goal-driven datasets, language is predictable from vision, causing conditional mutual information between instructions and actions to vanish (Information Collapse). LangForce uses…

Shijie Lian, Bin Yu, Xiaopeng Lin, Laurence T. Yang, et al.
Published
Jan 2026
Citations
14
Code
75 stars
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42

arXiv.org

TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers

TwinBrainVLA is a novel Vision-Language-Action (VLA) framework that addresses catastrophic forgetting in VLM fine-tuning for robotic control. It uses two isomorphic VLM pathways: a frozen 'Left Brain' (generalist) preserving pre-trained semantic knowledge, and a trainable 'Right Brain' (specialist) for motor control. An Asymmetric Mixture-of-Transformers…

Bin Yu, Shijie Lian, Xiaopeng Lin, Yuliang Wei, et al.
Published
Jan 2026
Citations
12
Code
29 stars
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43

arXiv.org

Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

Being-H0.5 is a foundational Vision-Language-Action (VLA) model for cross-embodiment generalization, introduced by the BeingBeyond Team. It uses a human-centric learning paradigm, treating human interaction traces as a universal 'mother tongue' for physical interaction. The model is trained on UniHand-2.0, the largest embodied pre-training recipe to date,…

Hao Luo, Ye Wang, Wanpeng Zhang, Sipeng Zheng, et al.
Published
Jan 2026
Citations
53
Code
1.1K stars
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44

NVIDIA

Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Fast-ThinkAct is an efficient reasoning framework for Vision-Language-Action (VLA) tasks that compresses lengthy chain-of-thought (CoT) reasoning into compact latent representations. It uses a teacher-student distillation approach, where a textual teacher VLM (trained with GRPO) provides reasoning traces, and a latent student VLM learns to generate…

Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, et al.
Published
Jan 2026
Citations
17
Code
Not linked
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45

arXiv.org

PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence

The paper introduces PhysBrain, a model that bridges vision-language models (VLMs) and physical intelligence for robotics by using human egocentric videos. The authors propose the Egocentric2Embodiment (E2E) Translation Pipeline, which converts raw egocentric videos into multi-level, schema-driven VQA supervision with evidence grounding and temporal…

Xiaopeng Lin, Shijie Lian, Bin Yu, Ruoqi Yang, et al.
Published
Dec 2025
Citations
8
Code
Not linked
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46

arXiv.org

PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image

PhysX-Anything is a novel framework that generates simulation-ready physical 3D assets from a single in-the-wild image. It uses a VLM-based generative model to predict geometry, articulation, and physical properties, and introduces a voxel-based representation that reduces token count by 193x, enabling explicit geometry learning without special tokens. The…

Ziang Cao, Fangzhou Hong, Zhaoxi Chen, Liang Pan, et al.
Published
Nov 2025
Citations
25
Code
914 stars
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47

Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems

Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization

The paper investigates how fine-tuning Vision-Language-Action (VLA) models on robotic tasks degrades the visual representations inherited from their base Vision-Language Models (VLMs). The authors show that naive action fine-tuning causes representation collapse and attention sink, leading to domain-specific forgetting of vision-language knowledge. They…

Nikita Kachaev, Mikhail Kolosov, Daniil Zelezetsky, Alexey K. Kovalev, et al.
Published
Oct 2025
Citations
28
Code
69 stars
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48

arXiv.org

RoboOmni: Proactive Robot Manipulation in Omni-modal Context

RoboOmni introduces cross-modal contextual instructions, a new setting for robotic manipulation where robots infer user intent from speech, environmental sounds, and visual cues rather than explicit commands. The authors propose a Perceiver-Thinker-Talker-Executor framework built on end-to-end omni-modal LLMs, which fuses auditory and visual signals for…

Siyin Wang, Jinlan Fu, Feihong Liu, Xinzhe He, et al.
Published
Oct 2025
Citations
8
Code
117 stars
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49

arXiv.org

GigaBrain-0: A World Model-Powered Vision-Language-Action Model

GigaBrain-0 is a Vision-Language-Action (VLA) foundation model that uses world model-generated data to reduce reliance on costly real-world robot data and improve generalization. It employs a mixture-of-transformers architecture with a pretrained PaliGemma2 VLM and a Diffusion Transformer for action generation, enhanced by RGBD input modeling and embodied…

GigaBrain Team, Angen Ye, Boyuan Wang, Chaojun Ni, et al.
Published
Oct 2025
Citations
42
Code
Not linked
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50

arXiv.org

Robot Learning: A Tutorial

This tutorial by Capuano et al. (University of Oxford, Hugging Face) provides a comprehensive overview of modern robot learning, tracing the shift from classical, model-based control to data-driven, learning-based paradigms. It covers foundational concepts in classical robotics, including forward/inverse kinematics and their limitations, then introduces…

Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, et al.
Published
Oct 2025
Citations
5
Code
563 stars
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