Pose-Anchored Optical Flow for Low-Latency Human Action Anticipation in Human-Robot Teaming proposes PoseOFF, a representation that captures local motion around human joints by conditioning optical flow extraction on pose. This structured motion representation aligns with human kinematics and improves early action recognition accuracy across multiple datasets and backbones. PoseOFF achieves comparable or better performance while observing less of the action sequence, making it suitable for real‑time, resource‑constrained robotic systems.
Human-robot interaction (HRI) requires robots to interpret human actions early in their execution in order to respond safely, efficiently, and naturally. However, many existing approaches to human act...
arXiv:2602. 19710v3 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level perception with sparse, embodiment-specific action supervision.
arXiv:2608. 16222v1 Announce Type: cross Abstract: Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions.
PACT is an end‑to‑end model that jointly learns human pose, contacts, and contact forces from monocular video. It augments a human reconstruction foundation model with learnable contact‑force tokens and a temporal transformer, and uses physics‑based supervision to enforce consistency between motion and forces. The authors also create a data annotation pipeline and a real‑world climbing benchmark, ForceWall, to train and evaluate the system, achieving state‑of‑the‑art performance and better generalization than staged approaches.
SocioGesture is a real‑time, adaptive system for recognizing social gestures in human‑robot interaction. It employs a compact, confidence‑aware body‑hand skeleton representation and a lightweight dual‑stream model that fuses body motion with hand articulation, enabling low‑latency onboard recognition. The model is trained with occlusion‑aware skeleton corruption to handle missing hands, occluded arms, and unstable keypoints, and it can expand its gesture vocabulary during deployment by saving uncertain interaction segments for offline labeling.
arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes...
BeyondRetarget is an end‑to‑end framework that learns to generate executable humanoid robot motions directly from monocular RGB videos, bypassing the need for an explicit human motion representation. By learning robot‑oriented implicit representations and incorporating a contact‑aware motion optimization mechanism, the method captures cross‑morphology motion structures and improves temporal consistency and physical plausibility. Experiments demonstrate that BeyondRetarget achieves higher execution success rates, lower latency, and greater accuracy and robustness in both simulation and real humanoid robots.
World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into do...
Being‑M0.7 is a latent world‑action model that learns from a large mixed‑modality human dataset (over 10,000 hours of video‑only, motion‑only, and paired video‑motion data) to predict future latent visual states and motion. The model is pre‑trained on human data, adapted to robot viewpoints during mid‑training, and then combined with an action expert via gated cross‑attention during post‑training to produce executable whole‑body commands. In experiments, Being‑M0.7 outperforms baselines on the SIMPLE benchmark and matches the best baseline on real‑world Unitree G1 loco‑manipulation tasks.
The paper introduces Skeleton-Language feature Pooling Switching, a weakly‑supervised vision‑language pretraining strategy for skeleton‑based zero‑shot spatio‑temporal action localization. It replaces video‑level pooling with instance‑level feature computation during inference, enabling the model to estimate unseen actions without costly annotations. Additionally, Scene‑Mixed Discriminative Contrastive Learning is proposed to separate actions at the instance level within mixed scenes using a MIL framework, and experiments on four public datasets confirm the method’s effectiveness.
arXiv:2609.19142v1 Announce Type: new Abstract: World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse...
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...