most citedLearning 2D Invariant Affordance Knowledge for 3D Affordance Grounding

6 citations · 6 across the 6 of their papers we have counts for

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cs.RO2026

InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization

Haoxiang Ma, Junhao Cai, Xiaoxu Xu +26

Unified models for robot manipulation aim to equip one policy with both the semantic priors of pretrained VLMs and the physical dynamics learned through future prediction. In pract…

cs.RO2026

EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets

Yichen Niu, Haoran Lv, Xinrui Zhang +12

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, geometr…

cs.RO2026

VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training

Siyuan Yang, Linzheng Guo, Ouyang Lu +10

Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Visio…

cs.RO2026

AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models

Yuhua Jiang, Shuang Cheng, Yan Ding +2

Vision-language-action (VLA) models have recently emerged as a powerful paradigm for building generalist robots. However, traditional VLA models that generate actions through flow…

cs.RO2026

U-ARM : Ultra low-cost general teleoperation interface for robot manipulation

Yanwen Zou, Zhaoye Zhou, Chenyang Shi +4

We propose U-Arm, a low-cost and rapidly adaptable leader-follower teleoperation framework designed to interface with most of commercially available robotic arms. Our system suppor…

cs.RO2025

MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm

Xin Liu, Bida Ma, Chenkun Qi +14

Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforc…