arXiv Computer Vision
PoseShield: Neural Collision Fields for Human Self-Collision Resolution
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arXiv AI
Sep 1
PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking
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 Computer Vision
Sep 18
DirtyMoCap: Robust Motion Capture from Unconstrained Markers
DirtyMoCap is a marker‑layout‑free framework that converts unordered, noisy optical motion capture markers into a fixed set of proxy anchors representing skeletal joints and body surface points. Using a recurrent sliding‑window architecture to track these anchors and a custom differentiable Gauss‑Newton solver to fit the SMPL‑H model, the method learns adaptive observation confidence, smoothness, and prior weights end‑to‑end. Experiments show that DirtyMoCap generalizes across arbitrary marker configurations, outperforms configuration‑specific baselines in joint and vertex accuracy, and achieves up to a 100× speedup over standard PyTorch implementations, enabling the creation of a temporally coherent Kung Fu motion dataset.
arXiv AI
Aug 18
Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
arXiv:2604. 07084v2 Announce Type: replace-cross Abstract: Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators.
arXiv Machine Learning
Jun 15
EqCollide: Equivariant and Collision-Aware Deformable Objects Neural Simulator
arXiv:2506. 05797v2 Announce Type: replace Abstract: Simulating collisions of deformable objects is a fundamental yet challenging task due to the complexity of modeling solid mechanics and multi-body interactions.
arXiv AI
Sep 10
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model
arXiv:2609.09158v1 Announce Type: cross Abstract: We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D pat...
arXiv AI
Jul 1
A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting
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.