arXiv Machine Learning
Jul 7

Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control

arXiv:2607. 04978v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning.

By Ruslan Rakhimov, George Bredis, Yuriy Maksyuta, Daniil Gavrilov
Hugging Face Trending Papers
Jul 6

Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control

Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins.

arXiv AI
Jun 16

LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

arXiv:2606. 15768v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change the scene.

By Jialei Chen, Kai Wang, Kang Chen, Shuaihang Chen, Feng Gao, Wenhao Tang, Zhiyuan Li, Weilin Liu, Zhuyu Yao, Boxun Li, Yuanbo Xu, Chao Yu
arXiv Computer Vision
Sep 25

RotVLA: Rotational Latent Action for Vision-Language-Action Model

RotVLA introduces a Vision‑Language‑Action framework that replaces discrete latent action encoding with a continuous rotational latent action representation on the group SO(n). This design provides continuity, compositionality, and structured geometry that better capture real‑world action dynamics, and a triplet frame learning scheme enforces meaningful temporal dynamics while preventing degeneration. Trained with 1.7 B parameters on large cross‑embodiment datasets, RotVLA achieves state‑of‑the‑art performance on LIBERO and RoboTwin2.0 benchmarks and shows strong real‑world manipulation results.

By Qiwei Li, Xicheng Gong, Xinghang Li, Peiyan Li, Quanyun Zhou, Hangjun Ye, Jiahuan Zhou, Yadong Mu
arXiv AI
Oct 21

UniWALM: Unified World Action Language Model for Embodied Robot Control

UniWALM is a unified world-action-language model that jointly learns video, action, and language in a single shared representation, using a mixture-of-transformers backbone with diffusion heads that co-denoise across modalities. By generating language as part of the same pass as action, the model closes a language‑as‑memory loop without an external planner, and introduces Flow Matching with Correction to maintain reliable action streams. Experiments show UniWALM outperforms baselines on compositional pick‑and‑place tasks and memory‑dependent benchmarks, and dramatically improves success on a real‑world Find‑the‑Item task when using generated textual memory.

By Rahul Sajnani, Diego Romeres, Mingyuan Zhang, Kai Gao, Carl Qi, Baichuan Huang, Srinath Sridhar, Dylan Randle, Lillian J. Ratliff, Jose Barreiros, Manikantan Nambi, Jeroen Vanbaar