2024
Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation
Ian Chuang*, Andrew Lee*, Dechen Gao, Iman Soltani (* equal contribution)
Workshop on Whole-body Control and Bimanual Manipulation @ CoRL 2024
International Conference on Robotics and Automation (ICRA) 2025
We introduce AV-ALOHA, a new bimanual teleoperation robot system that extends the ALOHA 2 robot system with Active Vision. This system provides an immersive teleoperation experience, with bimanual first-person control, enabling the operator to dynamically explore and search the scene and simultaneously interact with the environment. We conduct imitation learning experiments and our results show significant improvements over fixed cameras in tasks with limited visibility.
[project page] [arXiv] [code] [code (VR)] [video] [poster (workshop)] [BibTeX]
Active Vision Might Be All You Need: Exploring Active Vision in Bimanual Robotic Manipulation
Ian Chuang*, Andrew Lee*, Dechen Gao, Iman Soltani (* equal contribution)
Workshop on Whole-body Control and Bimanual Manipulation @ CoRL 2024
International Conference on Robotics and Automation (ICRA) 2025
We introduce AV-ALOHA, a new bimanual teleoperation robot system that extends the ALOHA 2 robot system with Active Vision. This system provides an immersive teleoperation experience, with bimanual first-person control, enabling the operator to dynamically explore and search the scene and simultaneously interact with the environment. We conduct imitation learning experiments and our results show significant improvements over fixed cameras in tasks with limited visibility.
[project page] [arXiv] [code] [code (VR)] [video] [poster (workshop)]
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation
Andrew Lee, Ian Chuang, Ling-Yuan Chen, Iman Soltani
Conference on Robot Learning (CoRL) 2024
InterACT is an imitation learning model that captures and extracts inter-dependencies between dual-arm joint positions and visual inputs. By doing so, InterACT guides the two arms to perform bimanual tasks with precision—independently yet in seamless coordination.
[project page] [arXiv] [poster] [BibTeX]
InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation
Andrew Lee, Ian Chuang, Ling-Yuan Chen, Iman Soltani
Conference on Robot Learning (CoRL) 2024
InterACT is an imitation learning model that captures and extracts inter-dependencies between dual-arm joint positions and visual inputs. By doing so, InterACT guides the two arms to perform bimanual tasks with precision—independently yet in seamless coordination.