Cross-Embodiment Robot Learning

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52 papers in the last four weeks, up 550% on the four weeks before. 0.5% of all new papers.

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DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training
Oct 8, 2026Junyan Li, Ruizhi Li, Yu Liu +6World Models for Robotics Action-Conditioned World Models
NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA) · Amap, Alibaba Group
VioLA: Learning Generalist Humanoid Control Policies from Human Data
Oct 8, 2026Mert Albaba, Jens Beißwenger, Anna Manasyan +6Humanoid Robot Control Robot Imitation Learning
Vesoma · ETH Zürich · MPI-IS +2
TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning
Oct 8, 2026Bo Chen, Huanzhang Hu, Junyang Ma +11Robotic Data Collection Cross-Embodiment Robot Learning
The University of Hong Kong · INFIFORCE · The Chinese University of Hong Kong (Shenzhen) +3
Being-M0.7: A Latent World-Action Model for Humanoid Robots
Oct 8, 2026Junpeng Yue, Boyuan Li, Yuxuan Wang +11Humanoid Loco-Manipulation World Action Models
BeingBeyond Team
Cross-Embodiment Robot Foundation World Models with Latent Actions
Oct 7, 2026Huang Huang, Sriram Yenamandra, Arjun Majumdar +9World Models for Robotics Action-Conditioned World Models
Department of Computer Science, Stanford University, US · Work partially done while at Meta FAIR Robotics.
RoboJEPA: Scaling Robotic Latent World Models
Oct 7, 2026Artem Zholus, Nicolas Beltran-Velez, Jianhao Yuan +9World Models Latent World Models
FAIR at Meta · Chandar Research Lab · Mila - Quebec AI Institute +2
LACE-CRAFT: Robot Co-Design with Actor Inheritance and Blackboard Collaboration
Oct 7, 2026Yuhan Wen, Jiawei Wang, Qixuan Zhang +3Robot Morphology-Control Co-Design Robot Policy Adaptation
Deemos Corporation · ShanghaiTech University · University of Cambridge +2
EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Oct 6, 2026Lihan Zha, Shresth Grover, Tenny Yin +7Cross-Embodiment Robot Learning Motion Representation Learning
Princeton University · Toyota Research Institute · Physical Intelligence
From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids
Oct 6, 2026Jiyeon Koo, Eunseom Pyo, Jeonghee Seo +2Cross-Embodiment Robot Learning Motion Retargeting
iRASC Lab., School of Computing, Gachon University, Seongnam 13120, Republic of Korea
AutodidactWAM: Cross-Modal Self-Distillation from Generated Video to Robot Actions
Oct 6, 2026Sergei Kurchev, Iaroslav Kolomiets, Miguel Altamirano Cabrera +2Cross-Modal Knowledge Distillation Cross-Embodiment Robot Learning
Skolkovo Institute of Science and Technology (Skoltech), Bolshoy Boulevard 30, bld. 1, Moscow 121205, Russia. · MWS R&D Center.
ExoBridge: Learning a Bare Hand to Hand-Worn Exoskeleton Mapping through Human Limb Coupling
Oct 6, 2026Ruitong Tian, Xianyao Li, Noah B. Wilson +2Cross-Embodiment Robot Learning Robotic Manipulation
Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL, USA · Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA
Silicon Language: A Robot-Native Knowledge Exchange Framework for Heterogeneous Robots
Oct 6, 2026Yi Liu, Xianglin Meng, Chang Chen +1Multi-Robot Systems Robot Skill Learning
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China · Yulin Saiyi Intelligent Technology Co., Ltd., Yulin, China · Yulin Intelligent Unmanned Equipment Innovation Center Co., Ltd., Yulin, China
The Robot Is Not Its Description: GaugeBench for Representation Robustness in Morphology-Aware Policies
Oct 6, 2026Rahath Malladi, Arshia Sangwan, Rajesh K. Gupta +1Robotic Policy Evaluation Cross-Embodiment Robot Learning
University of California San Diego · New York University
FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting
Oct 1, 2026Kyungmin Lee, Sibeen Kim, Dongyoon Hwang +6RL for Robotics Cross-Embodiment Robot Learning
KAIST AI · Holiday Robotics
Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch
Oct 1, 2026Yoshiki Takebayashi, Giovanni Perantoni, Hikaru Sasaki +2Cross-Embodiment Robot Learning Adversarial Imitation Learning
Division of Information Science, Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan · Department of Industrial Engineering of the University of Trento, Trento, Italy
NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields
Oct 1, 2026Shota Kobayashi, Koki Seno, Daichi Yashima +1Language-Conditioned Robot Manipulation Cross-Embodiment Robot Learning
Keio University, Japan
Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models
Sep 30, 2026Qi Lyu, Jiahua Dong, Hao Shen +9Robot Skill Learning Action-Conditioned World Models
Shenyang Institute of Automation, Chinese Academy of Sciences · Mohamed bin Zayed University of Artificial Intelligence · Anhui University +3
Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining
Sep 30, 2026Chongyang Xu, Zhao Wu, Jin Chen +9Humanoid Loco-Manipulation Cross-Embodiment Robot Learning
Alibaba Group · Sichuan University · Shanghai Innovation Institute +2
Dream4ACT: A Shared Visual Action Interface for Multi-Embodiment Video-Action Modeling
Sep 30, 2026Xiangyu Zhu, Jin Xu, Yue Guo +6Action-Conditioned Video Generation Cross-Embodiment Robot Learning
Beijing Humanoid Robot Innovation Center · Beijing Institute of Technology · Harbin Institute of Technology, Shenzhen +2
IronMind: Scaling Humanoid Dexterous Manipulation via Camera-Space Ego-Centric Pretraining
Sep 30, 2026Huimin Pan, Yufan Ren, Kunpeng Song +16Cross-Embodiment Robot Learning Vision-Language-Action Models
World Model Team, XPENG Robotics
Function beyond Form: Functional Correspondence for Cross-Embodiment Dexterous Grasp Generation
Sep 30, 2026Bolin Zou, Wenlong Dong, Mu Ai +4Robotic Grasping Cross-Embodiment Robot Learning
Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China. · Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden. · School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China. +1
Rho: A Foundation for Efficiently Adaptable VLA Models
Sep 29, 2026Rho Team, Simran Bagaria, Daphne Chen +11Robot Policy Adaptation Bimanual Robotic Manipulation
Microsoft Research
CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Sep 29, 2026Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger +6Robot Foundation Models Cross-Embodiment Robot Learning
VinRobotics · National University of Singapore · TU Darmstadt +3
WorldLine: Action-Driven Visual Simulation for Robotic Manipulation
Sep 29, 2026Shenghe Zheng, Wenbo Li, Jiyao Zhang +4Action-Conditioned Video Generation World Models for Robotics
The Hong Kong University of Science and Technology · Joy Future Academy · Peking University +1
EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation
Sep 29, 2026Yiming Jiang, Jin Chen, Chongyang Xu +3Humanoid Loco-Manipulation Long-Horizon Robotic Manipulation
Beihang University · Alibaba Group · Shanghai Innovation Institute +1
Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning
Sep 29, 2026Merve Atasever, Keyan Azbijari, Cagan Bakirci +6Robot Policy Learning Signal Temporal Logic
Department of Computer Science, University of Southern California Los Angeles, CA, USA · University of Florida Gainesville, FL, USA
Scale-Invariant Manipulability Shape Tracking Across Heterogeneous Manipulators
Sep 29, 2026Geunwoo Kwon, Dong-gyu Lee, Kai Li +2Cross-Embodiment Robot Learning Robotic Manipulation
Hanyang University, Seoul, Republic of Korea · Hanyang University ERICA, Ansan, Republic of Korea
SAKI: Skill Assembly and Kinematic Imitation from Human Videos for Long-Horizon Mobile Manipulation
Sep 28, 2026Yijie Lu, James Zhao, Weiming ZhiLong-Horizon Robotic Manipulation Mobile Manipulation
School of Computer Science, The University of Sydney, Australia · Australian Centre for Robotics, The University of Sydney, Australia
DexRoam: Learning Mobile Bimanual Dexterous Manipulation from Egocentric Whole-Body Human Demonstrations
Sep 28, 2026Rui Zhou, Yibo Yuan, Junkai Zhao +4Mobile Manipulation Bimanual Robotic Manipulation
The Hong Kong University of Science and Technology · Beijing Academy of Artificial Intelligence · Beihang University +2
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
Sep 28, 2026Prithwish Dan, Chenyang Ma, Wei ZhanReinforcement Learning Generalization in Robotic Manipulation
Applied Intuition · University of North Carolina at Chapel Hill · University of California, Berkeley

Oct 8, 2026, cs.ROJ/K move · Enter open · S save

DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training

Junyan Li, Ruizhi Li, Yu Liu, Xiangshuo Liu+5

NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA) · Amap, Alibaba Group

World Models for Robotics Action-Conditioned World Models Cross-Embodiment Robot Learning Synthetic Data Generation for Robotics

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We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction. Training such a model on existing robot datasets faces two obstacles: imprecise calibration can impair action following, while limited coverage of unsuccessful interactions can bias predictions toward successful outcomes. To improve action following across embodiments, we render action trajectories into image-space conditions and introduce offline geometric calibration to align these conditions with the target videos. To broaden interaction coverage, we introduce counterfactual post-training, modifying recorded action trajectories and generating future videos under a wider range of actions and contact configurations. To provide feedback on these predictions without paired ground-truth futures, we construct a human-annotated video dataset covering robot, object, and interaction defects and use it to train an embodied video reward model. Its scores guide reinforcement-learning post-training toward more physically plausible interaction outcomes. On AgiBot, DreamTrue attains state-of-the-art action following, while reducing the human-assessed interaction defect rate from from 48.12% to 6.25%. Notably, our model ranks first in the world model track of the AgiBot World Challenge 2026. The project page can be found at https://brave-eai.github.io/DreamTrue.

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