81 citations · 129 across the 13 of their papers we have counts for
13 papers
Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation
Xutian Deng, Junnan Jiang, Wen Cheng +1
As medical ultrasound is becoming a prevailing examination approach nowadays, robotic ultrasound systems can facilitate the scanning process and prevent professional sonographers f…
Mixline: A Hybrid Reinforcement Learning Framework for Long-horizon Bimanual Coffee Stirring Task
Zheng Sun, Zhiqi Wang, Junjia Liu +2
Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to l…
Improving Robotic Grasping Ability Through Deep Shape Generation
Junnan Jiang, Yuyang Tu, Xiaohui Xiao +4
Data-driven approaches have become a dominant paradigm for robotic grasp planning. However, the performance of these approaches is enormously influenced by the quality of the avail…
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
Junjia Liu, Yiting Chen, Zhipeng Dong +4
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movement…
Learning Ultrasound Scanning Skills from Human Demonstrations
Xutian Deng, Ziwei Lei, Yi Wang +1
Recently, the robotic ultrasound system has become an emerging topic owing to the widespread use of medical ultrasound. However, it is still a challenging task to model and to tran…
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Xutian Deng, Yiting Chen, Fei Chen +1
Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system…
Learning Friction Model for Magnet-actuated Tethered Capsule Robot
Yi Wang, Yuyang Tu, Yuchen He +4
The potential diagnostic applications of magnet-actuated capsules have been greatly increased in recent years. For most of these potential applications, accurate position control o…
Learning Based Adaptive Force Control of Robotic Manipulation Based on Real-Time Object Stiffness Detection
Zhaoxing Deng, Xutian Deng, Miao Li
Force control is essential for medical robots when touching and contacting the patient's body. To increase the stability and efficiency in force control, an Adaption Module could b…
Learning Friction Model for Tethered Capsule Robot
Yi Wang, Yuchen He, Xutian Deng +3
With the potential applications of capsule robots in medical endoscopy, accurate dynamic control of the capsule robot is becoming more and more important. In the scale of a capsule…
Learning Dynamical System for Grasping Motion
Xiao Gao, Miao Li, Xiaohui Xiao
Dynamical System has been widely used for encoding trajectories from human demonstration, which has the inherent adaptability to dynamically changing environments and robustness to…
Data Generation for Learning to Grasp in a Bin-picking Scenario
Yiting Chen, Miao Li
The rise of deep learning has greatly transformed the pipeline of robotic grasping from model-based approach to data-driven stream. Along this line, a large scale of grasping data…
Unknown Object Segmentation through Domain Adaptation
Yiting Chen, Chenguang Yang, Miao Li
The ability to segment unknown objects in cluttered scenes has a profound impact on robot grasping. The rise of deep learning has greatly transformed the pipeline of robotic graspi…
Motion Mappings for Continuous Bilateral Teleoperation
Xiao Gao, João Silvério, Emmanuel Pignat +3
Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smo…