arXiv:2606. 09108v1 Announce Type: cross Abstract: Many stages of the robotic lifecycle, from morphology synthesis to operation, rely fundamentally on the reachable workspace.
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MorphIK is a flow‑matching neural model that learns inverse kinematics for revolute‑joint kinematic chains it has never seen during training. Using a transformer to encode a robot’s morphology and target pose, the model generates pose solutions from noise and can be fine‑tuned with optimization to achieve sub‑centimeter accuracy. It also efficiently samples the robot’s null space, producing diverse configurations for the same pose.
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.
arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.
UniMo introduces a unified point‑cloud based framework for generating 3D motion that works for both humans and animals, overcoming challenges posed by diverse skeletal topologies and limited animal datasets. It converts parametric skeletons into unparametric representations and uses dynamic sampling to focus on active joints. The authors also release UniML3D, a large motion‑language dataset with 145,907 sequences and 433,388 captions, and demonstrate state‑of‑the‑art performance on multiple benchmarks.
arXiv:2606. 20246v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference.
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...