arXiv:2611.08372v1 Announce Type: cross Abstract: Visuomotor policies trained through imitation learning are highly sensitive to changes in camera viewpoint. This is particularly problematic for mobi...
arXiv:2610.18950v1 Announce Type: cross Abstract: Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains...
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.
arXiv:2611.05163v2 Announce Type: replace-cross Abstract: Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleopera...
MAVP (Map-Aware Visuomotor Policies) is a framework that enhances mobile manipulation by predicting explicit base-pose targets and tracking them with localisation feedback. It reconstructs a static map from teleoperated demonstrations, expressing base trajectories in a shared map frame to provide consistent spatial supervision. During execution, the policy uses RGB observations, joint states, and the robot’s current map-frame base pose to jointly predict target base poses, arm actions, and gripper actions, while a low‑level controller corrects deviations using feedforward motion and pose error feedback. Pose‑noise augmentation during training further improves robustness, and MAVP outperforms unanchored velocity control across six real‑world tasks and three policy families.
The paper introduces RoboTryOn, a method that transforms human egocentric videos into synthetic demonstrations for robots by virtually swapping the human with a target robot while preserving task, objects, and background. It uses autoregressive keyframe translation guided by human hand skeletons and an inverse dynamics model to generate action labels for policy learning. Experiments on various humanoid robots show that policies trained on these synthetic videos achieve up to 60% success on new tasks, matching teleoperated demonstration performance at roughly one‑third the cost.
arXiv:2606. 20118v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or geometry deviates from the training distribution.
Sim-and-Human Co-training (SimHum) is a method that combines simulation and human demonstration data to train bimanual manipulation policies. It first extracts kinematic priors from simulation and visual priors from human observations, then fine‑tunes on a small real‑robot dataset. With only 80 real‑robot episodes per task, SimHum achieves 62.5% success on out‑of‑distribution scenes across four tabletop tasks, outperforming real‑only training by 53.7% and improving the best single‑source baseline by 35.0% in a matched‑time study.
arXiv:2606. 17846v1 Announce Type: cross Abstract: Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale.
arXiv:2602.18742v2 Announce Type: replace-cross Abstract: Synthetic data generated by video generative models has shown promise for robot learning as a scalable pipeline, but it often suffers from no...
InternW0-Δ is a unified World Action Model that integrates pretrained visual dynamics, scene semantics, 4D geometry, and motion priors within a Mixture-of-Transformers framework to generate robot actions. It leverages a frozen VLM for semantic guidance, a 4D foundation model for geometric priors, and introduces Causal Imprint to learn future-relevant scene changes without future-video rollout. The model is pretrained on a newly curated 20K‑hour heterogeneous corpus of robot and human demonstrations, achieving superior performance on simulation benchmarks and real‑robot platforms.
arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-f...