arXiv Computer Vision
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jun 4
ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
arXiv:2603. 09170v2 Announce Type: replace-cross Abstract: Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data.
Hugging Face Trending Papers
Jun 29
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egocentric images, language commands, and robot-compatible kinematic trajectories, yet no existing data source provides this complete tuple at scale.
arXiv AI
Jun 30
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
arXiv:2606. 30645v1 Announce Type: cross Abstract: Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion.
arXiv AI
4d ago
EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation
arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has e...
arXiv AI
Sep 10
From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video
arXiv:2609.08636v1 Announce Type: cross Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
arXiv Computer Vision
Sep 21
ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation
ULTRA is a unified framework for autonomous humanoid whole-body locomotion and manipulation that overcomes limitations of prior methods by combining a physics-driven neural retargeting algorithm with a multimodal controller. The retargeting algorithm translates large-scale motion capture data into physically plausible humanoid motions, while the controller learns to handle both dense motion references and sparse task specifications using a range of sensory inputs, from accurate motion-capture states to noisy egocentric vision. In simulation and on a real Unitree G1 humanoid, ULTRA demonstrates improved generalization and robustness, enabling coordinated whole-body behavior from sparse intent without relying on test-time reference motions.