arXiv AI
CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
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arXiv AI
Sep 12
ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies
arXiv:2609. 11697v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment.
arXiv AI
Sep 16
SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating
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.
arXiv AI
Sep 25
CrossSafe: Towards Cross-Embodiment Latent Safety Filters
CrossSafe proposes embodiment-conditioned safety filtering that uses a Hamilton‑Jacobi reachability value function shared across robots while conditioning on each robot’s morphology and kinematics via a morphology‑aware latent representation. The method performs reachability analysis directly in latent space, enabling a single policy trained on multiple bimanual robot embodiments and manipulation tasks to generalize zero‑shot to a held‑out embodiment and reduce collision rates. Experiments on five embodiments and five tasks demonstrate that training with more embodiments improves generalization.
arXiv AI
Jun 9
PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
arXiv:2606. 08414v1 Announce Type: cross Abstract: Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment.
arXiv Machine Learning
Jun 9
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
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.
Hugging Face Trending Papers
Aug 17
HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL
Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulation. The high dimensionality and interdependence of humanoid motions make it challenging for conventional single-stage VLA architectures to coordinate locomotion, waist posture, and dual-arm manipulation effectively.