The paper argues that diffusion-based action policies can use a frozen, observation‑free backbone as a reusable trajectory prior, with task adaptation handled entirely by the conditioning pathway. By pretraining a general action head on forward‑kinematics data and then freezing it, the authors show that a single backbone can match or outperform normally trained models on MimicGen and LIBERO. Their experiments reveal that a small 5 M‑parameter MLP backbone can rival large U‑Net and transformer backbones, indicating that action backbones are often over‑parameterized and that image‑style architectures may not be the best fit for low‑dimensional action generation.
By Jian Zhou, Sihao Lin, Shuai Fu, Zerui Li, Gengze Zhou, Qi WU
Dynin‑Robotics introduces an omnimodal masked‑diffusion backbone, Dynin‑Omni, that jointly represents language, visual observations, goals, and actions as discrete tokens. By conditioning on different spans, the same model learns action prediction, next‑observation prediction, goal‑state prediction, and trajectory‑to‑instruction reconstruction, enabling test‑time scaling through goal prediction and action‑candidate evaluation. The system, pretrained on 1.33 million trajectories from 48 Open X‑Embodiment datasets, achieves competitive performance on LIBERO, zero‑shot LIBERO‑Plus, and a 78.4 % success rate on a Franka Research 3 robot, while a block‑parallel implementation speeds up action decoding by up to 29.2×.
By Hoeun Lee, Jaeik Kim, Jusang Oh, Jinhyeok Kim, Geon Choi, Hyeonggeun Kim, Jaeyoung Do
arXiv:2605. 13548v3 Announce Type: replace-cross Abstract: Existing robotic foundation models, while powerful, are predicated on an implicit assumption of temporal homogeneity: treating all actions as equally informative during optimization.
By Daojie Peng, Fulong Ma, Jiahang Cao, Qiang Zhang, Xupeng Xie, Jian Guo, Ping Luo, Andrew F. Luo, Boyu Zhou, Jun Ma
arXiv:2607. 27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change.
By Zuojin Tang, Feifan Luo, Haoyun Liu, Botai Yuan, Dekang Qi, Ronghan Chen, Yandan Yang, Tong Lin, Xinyuan Chang, Mu Xu, Bin Liu, De Ma, Zhiheng Ma
The paper introduces HWAM, a Humanoid World Action Model that jointly generates reference actions and the robot’s post‑execution proprioceptive state, thereby closing the action‑execution gap in hierarchical humanoid systems. HWAM is trained via three conditional paths—policy denoising, forward dynamics modeling, and inverse dynamics modeling—to align policy outputs, executed motion, and visual outcomes. Experiments on the LimX OLI humanoid show HWAM achieving a 70.6% success rate on Candy Picking, outperforming Fast‑WAM’s 43.3%.
By Yan Yang, Jikun Rong, Minzhao Zhu, Zheyi Zhao, Qirui Hu, Zihan Lan, Weixin Mao, Yinhao Li, Zhen Fu, Hua Chen
arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.
By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding
arXiv:2606. 27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation.
By Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao
arXiv:2607.02092v4 Announce Type: replace-cross
Abstract: Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves furt...
By Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng, Ningwei Bai, Qichen Yin, Hanbo Ma, Junkai Liu, Junkai Sun, Dongcheng Lyu, Yi Dong, Zezhi Tang
DriftOPD is a teacher‑free, rollout‑free framework that performs sequence‑level on‑policy distillation of continuous Vision‑Language‑Action (VLA) action experts. It decomposes the sequence‑level reverse‑KL divergence into a chunk‑level reverse‑KL term and a future‑potential term, optimizing them with a one‑step drifting objective and a Q‑function critic learned from offline demonstrations. Experiments on multiple VLA architectures in simulation and real‑world manipulation show that DriftOPD outperforms existing one‑step distillation baselines while matching the task success of multi‑step teacher policies.
By Youngjun Jun, Kyumin Choi, Youngmin Kim, Seonghyun Jin, Sunwoo Park, Jangho Park, Jong Chul Ye
The paper introduces Alignment‑Guided Flow Transformer (AGFT), a framework for Vision‑Language‑Action (VLA) models that explicitly enforces tri‑modal alignment among vision, language, and action through a dedicated alignment loss. AGFT bridges representational gaps across modalities, improving task adaptation and robustness, and employs a flow‑matching objective to reduce inference steps compared to diffusion‑based policies. Experiments on a large benchmark demonstrate that AGFT achieves higher success rates and lower inference latency than state‑of‑the‑art baselines, highlighting tri‑modal alignment as crucial for scalable VLA manipulation.
By Shengchao Hu, Peng Wang, Qiyang Zhou, Guodong Zheng, Yuqi Huang, Li Shen, Ya Zhang, Dacheng Tao
The paper introduces BAS‑VLA, a task‑semantic action calibration framework for vision‑language‑action models that addresses two failure modes: unnecessary action drift under appearance changes and insufficient behavioral change under semantic alterations. BAS‑VLA uses a breaking‑centered calibration core and a selective evidence‑gated preserving auxiliary to maintain performance on clean and semantics‑preserving conditions while suppressing stale‑task behavior. Experiments on OpenPI‑pi0.5 and LIBERO‑Object Milk‑Swap show high success rates on clean and preserved tasks, a dramatic drop under target‑object swaps, and improved robustness to style shifts from 42% to 70% without harming clean performance.
By Shuaijun Liu, Feiyang You, Chengyu Wu, Shuyang Hao, Chenglong Zhang, Jingyao Cai, Xingwei Chen, Ningxin Su
arXiv:2604. 17787v2 Announce Type: replace-cross Abstract: Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) policies generate actions within a single unified space.
By Tingzheng Jia, Kan Guo, Lanping Qian, Yongli Hu, Daxin Tian, Guixian Qu, Chunmian Lin, Baocai Yin, Jiapu Wang