HERO: Learning Humanoid End-Effector Control for Visual Whole-Body Open-Vocabulary Object Grasping
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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...
Whole-Body Dexterity (WBD) is a framework that enables robots to use their entire body and appendages as contact surfaces for grasping and manipulating objects. The approach generates a dataset of feasible whole-body grasps, trains reinforcement‑learning experts on a repositioning objective, and distills them into a single policy that relies on proprioception and object geometry. The distilled policy achieves zero‑shot transfer to a real Unitree G1 robot, successfully completing complex tasks such as stowing a box under one arm and moving it from a shelf to a table, with a 90% success rate in real‑world trials.
EgoJEPA is a three‑stage framework that learns embodiment‑agnostic representations from egocentric human videos, eliminating the need for hand‑crafted retargeting mappings. It first pre‑trains latent actions via visual prediction conditioned on hand‑motion differences, then models future visual dynamics and latent actions jointly, and finally adapts to specific robots with lightweight action adapters. Experiments on five manipulation tasks across three robot embodiments show a 74% average success rate versus 65% for a retargeting baseline, while cutting cross‑embodiment wall‑clock cost by 47.2%.
arXiv:2606. 08057v1 Announce Type: cross Abstract: Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets.
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
arXiv:2605. 31286v2 Announce Type: replace-cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments.