From the 1 of 10 linked papers with an AI index.
10 papers
Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments
Mingyu Liu, Zeju Li, Jiuhe Shu +4
The paper shows that smooth robot demonstrations can miss critical alignment moments, and proposes slowing down and resampling key motion segments, plus a spatio‑temporal feature c…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
Beining Han, Yu-Wei Chao, Erwin Coumans +5
We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and…
VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning
Binghao Huang, Jie Xu, Iretiayo Akinola +8
Humans excel at bimanual assembly tasks by adapting to rich tactile feedback -- a capability that remains difficult to replicate in robots through behavioral cloning alone, due to…
Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Sirui Xu, Yu-Wei Chao, Liuyu Bian +4
Hand-object motion-capture (MoCap) repositories offer large-scale, contact-rich demonstrations and hold promise for scaling dexterous robotic manipulation. Yet demonstration inaccu…
GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao +7
Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize acro…