arXiv:2503. 15371v2 Announce Type: replace-cross Abstract: Robotic manipulation of unfamiliar objects in new environments is challenging due to limited generalisation capabilities.
arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.
FuncBridge is a two‑stage framework that addresses functional generalization in robotics by decoupling functional reasoning from action execution. It learns to predict generalizable 2D keypoint trajectories from action‑free data and then grounds these trajectories into robot actions with limited demonstrations. Across a benchmark of ten tools and three functions—hitting, sweeping, and hooking—FuncBridge outperforms state‑of‑the‑art methods on unseen tools in both simulation and real‑world tests.
KeyGen is a framework that learns canonical 3D keypoints from point clouds to create structured, object‑centric representations for policy learning in robotic manipulation. By conditioning a visuomotor diffusion policy on these keypoints and object geometry, it predicts full manipulation trajectories that maintain geometric correspondence across different object instances. Experiments on a photorealistic simulation benchmark with three tasks show that KeyGen outperforms prior methods on both seen and unseen objects, scales with more demonstrations, remains robust to rescaling, and performs well in real‑world manipulation.
arXiv:2606. 28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions.
UniCross proposes a unified framework that models the four canonical dexterous manipulation skills—grasping, relocation, in-hand rotation, and in-hand translation—within a single formulation sharing state, action spaces, and objective structure. This approach enables distillation of a single cross-skill policy that performs well across all skills, generalizes to unseen objects, remains robust to disturbances, and can chain skills into long-horizon manipulation without policy switching. The framework also transfers effectively across different hand morphologies, suggesting that these skills can be viewed as instantiations of a shared task formulation.
arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.
arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.
arXiv:2607. 26903v1 Announce Type: new Abstract: The key bottleneck in embodied AI is not model architecture but data.
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.
arXiv:2606. 04269v1 Announce Type: cross Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes.
arXiv:2608. 19776v1 Announce Type: cross Abstract: Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks.
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized.