arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.
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.
arXiv:2506. 12851v3 Announce Type: replace-cross Abstract: Humanoid robots are promising to acquire various skills by imitating human behaviors.
arXiv:2605. 23733v2 Announce Type: replace-cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity.
ViT-H is a video‑to‑humanoid control framework that generates humanoid reference motions directly from monocular human videos using a diffusion model, eliminating the need for separate reconstruction, retargeting, and tracking stages. The system incorporates motion‑tracking feedback to fine‑tune the generator via preference optimization, and an autoregressive variant enables real‑time teleoperation with sub‑400 ms latency. Experiments on the Unitree G1 robot show that ViT‑H surpasses existing reconstruction‑retargeting‑tracking pipelines in motion quality and executability, and its performance scales positively with model size and data volume.
arXiv:2603.19305v3 Announce Type: replace-cross Abstract: Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation mo...
arXiv:2607. 15163v1 Announce Type: cross Abstract: Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist embodied agents.
arXiv:2511. 07820v4 Announce Type: replace-cross Abstract: Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid control.
arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.
arXiv:2509. 15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections.
arXiv:2607. 03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control.
SafeFlow is a real‑time, text‑driven humanoid control framework that blends physics‑guided motion generation with a three‑stage safety gate. It uses Physics‑Guided Rectified Flow Matching in a VAE latent space to produce physically executable trajectories, accelerates sampling with Reflow, and filters unsafe outputs via semantic OOD detection, directional sensitivity checks, and hard kinematic constraints before handing them to a motion‑tracking controller. Experiments on the Unitree G1 show that SafeFlow achieves higher success rates, better physical compliance, and faster inference than diffusion‑ and retargeting‑based baselines while maintaining motion diversity.