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20222025
most citedEnd-to-End Affordance Learning for Robotic Manipulation

4 citations · 9 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.RO2025

Robotic Assistant: Completing Collaborative Tasks with Dexterous Vision-Language-Action Models

Boshi An, Chenyu Yang, Robert Katzschmann

We adapt a pre-trained Vision-Language-Action (VLA) model (Open-VLA) for dexterous human-robot collaboration with minimal language prompting. Our approach adds (i) FiLM conditionin…

cs.RO2025

Arnold: A multi-task, multi-embodiment muscle transformer policy

Boshi An, Alberto Silvio Chiappa, Merkourios Simos +2

Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge. Recent machine learning breakthroughs have heralded in-s…

cs.RO2025★ 1 cited

RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning

Haoran Geng, Feishi Wang, Songlin Wei +34

Data scaling and standardized evaluation benchmarks have driven significant advances in natural language processing and computer vision. However, robotics faces unique challenges i…

cs.RO2023★ 2 cited

ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection

Xiaoqi Li, Yanzi Wang, Yan Shen +6

In the realm of future home-assistant robots, 3D articulated object manipulation is essential for enabling robots to interact with their environment. Many existing studies make use…

cs.RO2023★ 1 cited

RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation

Boshi An, Yiran Geng, Kai Chen +3

Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this con…

cs.RO2022★ 4 cited

End-to-End Affordance Learning for Robotic Manipulation

Yiran Geng, Boshi An, Haoran Geng +3

Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can…