activity
20242026
most citedAction Tokenizer Matters in In-Context Imitation Learning

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.RO2026

EgoJEPA: Beyond Retargeting for Dexterous Manipulation from Egocentric Videos

Meng Cao, Can Zhao, Jinjie Wei +12

Egocentric human videos offer a scalable alternative to costly robot demonstra- tions, yet transferring their manipulation priors to robot control remains chal- lenging. The common…

cs.RO2026

The Robot Data Factory

Sami Haddadin, Ivan Laptev, Ian Reid +18

Physical AI requires more than increasingly large robot datasets: intelligent robots acquire knowledge through continuous interaction with the physical world. We argue that the def…

cs.RO2026

ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation

Yu Sun, Meng Cao, Yang Ping +24

Vision-Language-Action (VLA) models and world-action models have emerged as central paradigms for general-purpose robotic intelligence, yet their empirical progress remains constra…

cs.RO2025

eVGGT: An Efficient Geometry-Aware Vision Encoder for Visuomotor Policies

An Dinh Vuong, Minh Nhat Vu, Ian Reid

Geometry-grounded vision models, such as VGGT, have emerged as robust visual encoders, providing essential geometric priors for robotic manipulation. However, the high computationa…

cs.RO2025★ 1 cited

Action Tokenizer Matters in In-Context Imitation Learning

An Dinh Vuong, Minh Nhat Vu, Dong An +1

In-context imitation learning (ICIL) is a new paradigm that enables robots to generalize from demonstrations to unseen tasks without retraining. A well-structured action representa…

cs.RO2024

GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning

Huy Hoang Nguyen, An Vuong, Anh Nguyen +2

Grasp detection is a fundamental robotic task critical to the success of many industrial applications. However, current language-driven models for this task often struggle with clu…