Being-H0.7: A Latent World-Action Model from Egocentric Videos

• URL: http://arxiv.org/abs/2605.00078v1
• Date: Thu, 30 Apr 2026 14:16:15 GMT
• Title: Being-H0.7: A Latent World-Action Model from Egocentric Videos
• Abstract summary: We present Being-H0.7, a latent world-action model that brings future-aware reasoning into VLA-style policies.<n>Being-H0.7 inserts learnable latent queries between perception and action as a compact reasoning interface.
• Score: 32.77431338471086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supervision often encourages shortcut mappings rather than representations of dynamics, contact, and task progress. Recent world-action models introduce future prediction through video rollouts, yet pixel-space prediction is a costly and indirect substrate for control, as it may model visual details irrelevant to action generation and introduces substantial training or inference overhead. We present Being-H0.7, a latent world-action model that brings future-aware reasoning into VLA-style policies without generating future frames. Being-H0.7 inserts learnable latent queries between perception and action as a compact reasoning interface, and trains them with a future-informed dual-branch design: a deployable prior branch infers latent states from the current context, while a training-only posterior branch replaces the queries with embeddings from future observations. Jointly aligning the two branches at the latent reasoning space leads the prior branch to reason future-aware, action-useful structure from current observations alone. At inference, Being-H0.7 discards the posterior branch and performs no visual rollout. Experiments across six simulation benchmarks and diverse real-world tasks show that Being-H0.7 achieves state-of-the-art or comparable performance, combining the predictive benefits of world models with the efficiency and deployability of direct VLA policies.

Related papers

• DUET-DINO: Simultaneous Cross-View World Modeling for Latent Planning in Robot Manipulation [19.179682667653328]
  Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control.<n>We introduce DUET-DINO, a simultaneous cross-view latent world model that jointly learns action-conditioned predictions from static side- and wrist-camera observations.<n>By exploiting complementary global scene and gripper-centric information, DUET-DINO enables latent planning over the full 7-DoF action space.
  arXiv  Detail & Related papers  (2026-09-09T17:41:38Z)
• Foresight Without Seeing: Latent Futures for World Action Models [31.423405133926334]
  We propose ForeWAM, a dynamics-conditioned direct-policy WAM that provides predictive context for action generation without decoding future videos.<n>We also introduce dynamics supervised by a frozen latent action teacher, encouraging the implicit future states to capture interaction-induced transitions such as object motion, contact changes, and task progress.<n>Without embodied robot data pretraining, the standard and accelerated variants of ForeWAM achieve average success rates of 96.7% and 96.9% on LIBERO, respectively.
  arXiv  Detail & Related papers  (2026-08-12T03:27:43Z)
• FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models [21.446753234768824]
  Existing visual methods predict future visual states but lack explicit motion guidance.<n>We propose FoMoVLA, a framework that augments VLA representations with explicit foresight-temporal supervision.
  arXiv  Detail & Related papers  (2026-07-16T09:04:50Z)
• InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization [81.9611022323101]
  We present InternVLA-A1.5, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation.<n>Future prediction is recast as a latent-querying problem, where a small set of learnable foresight tokens condenses the task-relevant future into a compact latent code under the supervision of a frozen pretrained video generation model.<n>Pretrained on 1.2M robot episodes and 3M multimodal samples, InternVLA-A1.5 achieves the best overall results on all six simulation benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T12:25:30Z)
• From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation [88.39072412680633]
  We propose MoLA, a control-oriented interface that transforms imagined future videos into executable representations.<n>We evaluate our approach on simulated benchmarks and real-world robot manipulation tasks.
  arXiv  Detail & Related papers  (2026-05-12T14:15:16Z)
• Learning Vision-Language-Action World Models for Autonomous Driving [15.103497388527943]
  Vision-Language-Action (VLA) models have recently achieved notable progress in end-to-end autonomous driving.<n>VLA models often lack explicit modeling of temporal dynamics and global world consistency.<n>We present VLA-World, a simple yet effective VLA world model that unifies predictive imagination with reflective reasoning.
  arXiv  Detail & Related papers  (2026-04-10T07:38:05Z)
• LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning [51.969318585152116]
  LatentPilot exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics.<n>We propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution.<n> Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene.
  arXiv  Detail & Related papers  (2026-03-31T02:21:59Z)
• GigaWorld-Policy: An Efficient Action-Centered World--Action Model [50.107640832046464]
  GigaWorld-Policy is an action-centered WAM that learns 2D pixel-action dynamics while enabling efficient action decoding, with optional video generation.<n> Experimental results on real-world robotic platforms show that GigaWorld-Policy runs 9x faster than the leading WAM baseline, Motus.
  arXiv  Detail & Related papers  (2026-03-18T00:52:02Z)
• FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model [73.03346643967309]
  We argue that effective joint visuomotor predictive modeling requires both temporal continuity and visually-conditioned supervision decoupling.<n>FutureVLA is designed to extract joint visuomotor embeddings by first decoupling visual and motor information.<n>In the post-training stage, we employ a latent embeddings alignment strategy, enabling diverse downstream VLA models to internalize these temporal priors.
  arXiv  Detail & Related papers  (2026-03-11T12:39:55Z)
• Chain of World: World Model Thinking in Latent Motion [24.24061036481793]
  Vision-Language-Action (VLA) models often overlook the predictive and temporal-causal structure underlying visual dynamics.<n>We introduce CoWVLA, a new "Chain of World" paradigm that unifies world-model temporal reasoning with a disentangled latent motion representation.<n>CoWVLA outperforms existing world-model and latent-action approaches and achieves moderate computational efficiency.
  arXiv  Detail & Related papers  (2026-03-03T17:52:06Z)
• Ego-centric Predictive Model Conditioned on Hand Trajectories [52.531681772560724]
  In egocentric scenarios, anticipating both the next action and its visual outcome is essential for understanding human-object interactions.<n>We propose a unified two-stage predictive framework that jointly models action and visual future in egocentric scenarios.<n>Our approach is the first unified model designed to handle both egocentric human activity understanding and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-08-27T13:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.