TOPIC · ROBOTICS · 40 PAPERS
LLMsReasoningAlignmentAgentsMultimodalVisual generationVision & 3DRobotics & embodied AIEfficient training & inferenceSafety & interpretabilityAI for ScienceCode & SWEBenchmarks & dataReinforcement learning
#07 ▲388 ★116 ❝2 2026-09-28
A survey of in-context learning for robots, grouping methods into four families by how context is turned into action.
Knowin AI · Haojian Huang, Zexi Li, Junhao Guo +36
#11 ▲459 ★218 ❝0 2026-10-15
Evaluates GPT-6-Astra on 12 robotics benchmarks and discusses what a general model's embodied competence means for the field.
SenseNova · SenseNova, Joint Research Team, Zhongang Cai +59
#12 ▲113 ★2.8k ❝1 2026-10-07
Long-WAM scales the context of world-action models under real-time limits and finds autoregressive video pretraining makes long history pay off.
NVIDIA · Wei Huang, Bohan Zhang, Chenzhi Liu +13
Robotics & embodied AIEfficient training & inference arXiv Code HF
#14 ▲310 ★140 ❝2 2026-09-30
GroundingPI is a 4B grounding foundation model that outputs points and boxes as tokens to give robots fast, precise targeting.
XPENG AI · Qize Yu, Lianrui Fan, Boyu Chen +23
#32 ▲313 ★84 ❝0 2026-10-16
HexaShell keeps the harness thin so a coding agent drives a two-arm robot via a command line and writes its own tools after failures.
HexaFuture · Hongcheng Gao, Alex Zhou, Jay Zhu +13
#45 ▲140 ★82 ❝0 2026-09-28
Finds latent world-action models lose the generalisation benefits of explicitly generating future frames at test time.
Tsinghua-LeapLab · Renping Zhou, Zanlin Ni, Zihao Fan +8
#54 ▲112 ★54 ❝4 2026-09-28
Argues for expressing robot tasks as inspectable, revisable code so self-evolving coding agents can move into the physical world.
HexaFuture · Hongcheng Gao, Jingjing Zhou, Zelin Zheng +10
#59 ▲83 ★131 ❝3 2026-10-01
UniWAM unifies a physical reasoner, a world generator and an action predictor in one model for robot control.
HKUSTGZ · Wenxuan Song, Jiayi Chen, Jingbo Wang +13
#65 ▲34 ★637 ❝9 2026-09-29
Systematically tests GPT-6 Astra as an embodied policy across six domains, alone and combined with learned policies.
Galbot · Galbot Team, Xuchuan Chen, Xiaoqian Cheng +31
#72 ▲179 ★36 ❝0 2026-10-19
Ropedia W0 is an egocentric loco-manipulation world foundation model spanning humans and robots, trained on 10k hours of human and 3k hours of robot data.
Ropedia · Tianqi Liu, Hanfei Zhao, Qifeng Liang +12
#79 ▲151 ★70 ❝0 2026-09-28
PanoVLN studies how to make vision-and-language navigation actually benefit from panoramic observations.
Zhejiang University · Zhen Wang, Changpeng Wang, Zhe Liu +5
#88 ▲154 ★34 ❝0 2026-10-08
Reflex enables humanoids to react with whole-body control from egocentric vision, such as catching a thrown box.
University of Washington · Taoyang Jia, Weikai Huang, Linxin Song +6
#93 ▲170 ★18 2026-10-19
EgoXperience jointly reconstructs observer motion, scene geometry and hand-object interaction from egocentric video, with an auto-annotation engine and benchmark.
Ropedia · Xinhao Ji, Hui En Pang, Hao Li +12
Vision & 3DRobotics & embodied AIBenchmarks & data arXiv Code HF
#103 ▲86 ★198 ❝0 2026-10-08
SuperNav lets an MLLM interpret requests and decide while delegating motion to navigation tools, generalising across tasks and scenes.
zju3dv · Jinkai Zhang, Jingyi Xu, Yuanhong Yu +5
#108 ▲75 ★287 ❝0 2026-10-08
LeWAM is an end-to-end JEPA world-action model that supports both direct policy execution and efficient planning.
LeWAM · Minghao Fu, Tavis Siebert, Eryk Halicki +1
#115 ▲52 ★51 ❝5 2026-10-01
Has robot agents compose primitives into locally retried Python cells, raising success while cutting token use.
DAGroup-PKU · Ruiyang Si, Jianxin Bi, Shunyu Yang +12
Robotics & embodied AIAgentsEfficient training & inference arXiv Code HF
#121 ▲89 ❝11 2026-09-19
RoboDawn exposes robot control as simple discrete commands to an agentic VLM, testing whether digital intelligence transfers to the physical world.
Affiliation unverified · Meng-Hao Guo, Zhe-Han Mo, Jia-Jun Wang +6
#128 ▲88 ★51 ❝0 2026-09-20
Proposes Grounded Action Models, robot foundation models built on 3D grounding and promptable with language, points or boxes.
National University of Singapore · Gehao Zhang, Weikai Huang, Shailesh Shailesh +3
#142 ▲72 ★104 ❝0 2026-10-01
ViGAR splits long-horizon manipulation into a visual subgoal planner and a subgoal executor for compositional, in-context robot tasks.
DAGroup-PKU · Shukai Gong, Xuanran Zhai, Yintianrun Zhang +16
#156 ▲84 ★40 2026-10-19
ScaleNav learns goal-directed visual dynamics from internet egocentric videos for language-conditioned robot navigation.
Affiliation unverified · Yihui Yang, Yuanhong Yu, Jiawei Wang +7
#162 ▲72 ★21 ❝1 2026-10-08
A multi-agent egocentric world model that generates synchronised first-person streams of agents interacting in a shared world.
KAIST AI · Dahyun Chung, Siyoon Jin, Hyunwook Choi +5
#165 ▲98 ❝1 2026-10-01
ProWAM predicts actions plus an ordered sequence of sparse visual subgoals, guiding long-horizon control more efficiently than full video.
Meta · Fei Zhang, Zhaochong An, Duncan Frost +5
Robotics & embodied AIEfficient training & inference arXiv HF
#175 ▲110 ★6 ❝0 2026-10-13
GreenWAM is a robotics world foundation model using a shared joint action space across 20 robot families, serving as both world model and policy.
Sber Robotics Center · Gennady Sidorov, Valerii Kornilov, Denis Grankin +2
#177 ▲49 ★54 ❝1 2026-09-29
EVO-WAM adapts world-action models to new tasks by learning from their own verified generated video-action trajectories.
Harbin Institute of Techonology, Shenzhen · Shiyang Zhou, Xionghao Wu, Wenbo Li +11
#185 ▲87 ★5 ❝1 2026-09-28
Exposes each skill policy's structural prior to the agent so few-shot robots generalise both compositionally and per skill.
National University of Singapore · Puming Jiang, Tianrun Hu, Haozhe Du +4
#189 ▲23 ★2.9k ❝0 2026-10-05
RealtimeWAM generates actions in one step with asynchronous inference to make world-action models real-time.
Nanyang Technological University · Chengtao Lv, Jinyang Du, Shuyi Feng +7
Robotics & embodied AIEfficient training & inference arXiv Code HF
#204 ▲74 ★17 ❝0 2026-10-09
EmbodiedCLBench is the first embodied continual-learning benchmark, testing whether agents recombine learned skills on new tasks.
PKU-VaLuE-Lab · Jie Huang, Yanan Chen, Ruixun Liu +6
#205 ▲64 ★18 ❝0 2026-09-29
Clearly defines what robot in-context learning should follow and offers a minimalist, reproducible recipe for manipulation.
Affiliation unverified · Minxing Li, Minghao Han, Weizhi Zhao +10
#219 ▲40 ★153 ❝0 2026-09-29
TERRA retargets motion with terrain to musculoskeletal models so muscle-driven agents can walk on complex ground.
Mathis Group @ EPFL · Merkourios Simos, Chengkun Li, Bianca Ziliotto +1
#223 ▲43 ★111 ❝0 2026-10-14
A survey of agentic robots organised around model, data, environment and harness.
Affiliation unverified · Chonghan Qin, Dongming Wu, Kaituo Feng +13
#226 ▲64 ★22 ❝0 2026-10-15
VLA-Δ explicitly supervises physical state transitions to reduce VLA overfitting to observation-action pairs.
ZJU-OmniAI · Qi Lu, Jiarun Li, Ce Zhang +5
#233 ▲66 ★5 ❝1 2026-09-25
DMM refines joint actions in decentralised multi-agent pathfinding by iterative intent denoising, avoiding incompatible combinations.
Affiliation unverified · Valeriy Vyaltsev, Anton Andreychuk, Taisia Zlotnikova +3
#234 ▲40 ★41 ❝1 2026-10-07
RobotWorld uses 84 simulated tasks (manipulation, locomotion, driving, aerial) to test multimodal agents at robot use.
Affiliation unverified · Zhiqin Yang, Chenxin Li, Xiaomeng Hu +30
#241 ▲88 ❝0 2026-10-02
NAVA-WAM pretrains the action policy directly from observation-only videos, reducing reliance on action-labelled robot data.
Meta · Zhaochong An, Fei Zhang, Menglin Jia +10
#260 ▲45 ★19 ❝1 2026-10-07
RLHND uses a video foundation model to estimate hand pose and touch from egocentric video, making human videos more useful for robots.
RLWRLD · Seungjun Moon, Subin Jeon, Sangwoo Kim +2
#267 ▲115 ❝0 2026-09-29
MotorMind scaffolds general vision-language models for zero-shot robot manipulation without specialised VLA training.
University of Illinois at Urbana-Champaign · Bingxuan Li, Siqi Song, Yizhuo Wu +3
#270 ▲112 2026-10-19
Collective experience learning lets 96 deployed robots share execution data, retrieving successful segments for failures to fine-tune a common policy without new demos.
Affiliation unverified · Chao Yu, Shu'ang Yu, Hao Lin +41
#273 ▲60 ★9 ❝0 2026-10-18
VLA-Rolling reuses the unexecuted action tail as the starting point for denoising, cutting steps for frozen flow-matching VLAs while often raising success.
ZJU-OmniAI · Sicheng He, Yi Pan, Qiuting Yu +10
Robotics & embodied AIEfficient training & inference arXiv Code HF
#274 ▲46 ★22 ❝0 2026-10-10
BridgeWAM links video and action experts through 32 latent bridge queries instead of dense layer-wise coupling, enabling a two-layer action head.
Fudan University · Jiaxin Fang, Ruitong Liu, Zeyu Lai +6
#280 ▲42 ★17 ❝0 2026-10-20
VPM is a world model that jointly forecasts multi-view future video and 3D point trajectories, extended to robot control.
Affiliation unverified · Byungwoo Jeon, Sunghwan Hong, Dohyeon Kim +10
Data updated: Latest AI papers(UTC+8)