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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.RO2026

Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

Yu Qi, Zhang Ye, Xinyi Xu +6

Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferring to salient cues rather than…

cs.RO2026

Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation

Haojie Huang, Linfeng Zhao, Haotian Liu +9

Pix2Act is an imitation‑learning approach that predicts continuous 2D keypoint trajectories in camera images and recovers 3D end‑effector poses via triangulation, using equivariant…

cs.RO2026

Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification

Haojie Huang, Zhang Ye, Linfeng Zhao +7

The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions. A…

cs.RO2026

WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT

Zezhong Qian, Xiaowei Chi, Yu Qi +3

Recent World-Action (WA) models demonstrate strong generalization ability and data efficiency, but they typically rely on expert trajectories for training. This reliance limits the…

cs.CV2026

Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis

Yu Qi, Haibo Zhao, Ziyu Guo +17

Understanding the capability bottlenecks of embodied multimodal large language models (MLLMs) is crucial for improving embodied agents. However, existing embodied benchmarks mainly…

cs.RO2026

ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter

Yaoyao Qian, Xupeng Zhu, Ondrej Biza +5

Robotic grasping in cluttered environments remains a significant challenge due to occlusions and complex object arrangements. We have developed ThinkGrasp, a plug-and-play vision-l…