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From the 1 of 141 linked papers with an AI index.

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20242026
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cs.RO2026

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping

Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou +2

Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning. Existing interaction-ce…

cs.RO2026

Hermite Curves as Trajectory Priors for Vision-Language-Action Models

Qi Lv, Jianming Xing, Zhao Yang +5

Despite recent progress in Vision-Language-Action (VLA) models for robotic manipulation, the action chunk remains a weakly structured interface. Existing work typically flatten eac…

cs.RO2026

Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos

Danze Chen, Yanzhe Chen, Qiming Huang +3

Vision-Language-Action (VLA) models require large-scale video-action pairs, yet real teleoperation remains scarce. While generated robot videos offer a scalable alternative, existi…

cs.RO2026

ActionMap: Robot Policy Learning via Voxel Action Heatmap

Pei Yang, Hai Ci, Yanzhe Chen +3

Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state int…

cs.RO2026

World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy

Xiaokang Liu, Zechen Bai, Hai Ci +2

Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to extensive rollouts, resets, supervi…

cs.RO2026

World Action Models: The Next Frontier in Embodied AI

Siyin Wang, Junhao Shi, Zhaoyang Fu +11

Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explic…