arXiv Computer Vision
EgoForesight: Effectively Scaling Policy Learning with Noisy Egocentric Actions
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arXiv AI
Oct 12
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
LAPT-Scale introduces a latent action pre‑training framework that leverages large‑scale egocentric human videos to train embodied robotic policies. The method comprises LAPT‑Scale‑LAM, which learns continuous latent actions by modeling visual transitions in human videos, and LAPT‑Scale‑Policy, which aligns these latent actions with robot embodiment and fine‑tunes for specific tasks. Using 23.3K hours of video, the approach improves real‑robot success rates from 63.3% to 85.0% and generalizes to unseen visual conditions.
arXiv Computer Vision
Oct 5
Native Action-Prior Learning from Videos for World Action Models
The paper introduces NAVA-WAM, a method that learns action priors directly from observation-only videos to pretrain action policies for robots. It uses a two-stage training process: first, it pretrains on videos with future‑video flow‑matching supervision to learn action‑relevant priors, and second, it fine‑tunes with action‑labeled demonstrations for robot control. Experiments show that NAVA‑WAM outperforms previous methods in both in‑distribution and out‑of‑distribution scenarios, achieving strong action‑label efficiency and real‑robot generalization.
arXiv AI
Jun 2
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
arXiv:2606. 00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models.
arXiv Computer Vision
Oct 16
Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation
The paper introduces P³-WAM, a unified world action model for egocentric dexterous manipulation that integrates 3D perception, action-conditioned prediction, and goal-directed planning. It operates in three modes—Perceive, Predict, and Plan—using a mixture-of-transformers architecture that shares base weights and adapts to modality-specific inputs. Experiments demonstrate that unified training improves performance across all three capabilities, outperforming specialized baselines on 3D hand reconstruction and action-conditioned video prediction while producing more instruction‑adherent manipulation videos.
arXiv AI
Oct 15
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control r...
arXiv Computer Vision
Oct 12
Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics
The paper introduces HF‑WM, an action‑conditioned world model that learns from large‑scale egocentric human videos and incorporates failure dynamics through post‑training. It bridges the embodiment gap with a dual‑branch framework that couples video generation to stabilized pointflow prediction, unifying motion representation across humans and robots while reducing egocentric camera motion. Failure modeling is enhanced by Group Relative Policy Optimization on perturbed actions, guided by action alignment and physical plausibility rewards, enabling the model to anticipate unsuccessful executions. Experiments show HF‑WM outperforms existing methods in action‑conditioned video generation and is useful for evaluating policies in downstream robotic tasks.