19 accepted papers
Dexterous in-hand manipulation, especially involving interactions between grasped objects and external environments, remains a formidable challenge in robotics. This study tackles the complexities of in-hand manipulation under extrinsic contact through a representative three-finger handwriting task.…
Robot scooping is a challenging and important task in robotic tool manipulation research due to the complex relationship between the robot, the tool, and target objects/environment. Taking into account different tools, different target objects and varying environments, the required scooping manipula
Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focu
Dexterous in-hand manipulation, especially involving interactions between grasped objects and external environments, remains a formidable challenge in robotics. This study tackles the complexities of in-hand manipulation under extrinsic contact through a representative three-finger handwriting task.
Robots and prostheses are increasingly designed with curvilinear surfaces for functional, aesthetic, aerodynamic, and safety reasons. Electronic skins (e-skins) capable of sensing contact location and pressure across complex, non-developable surfaces are essential for empowering next-generation robo…
This paper proposes a new approach for improving robotic manipulation tasks that require both precision and high compliance through multisensory fusion. By integrating visual, force, and tactile feedback, our approach enhances performance in delicate insertion tasks. We introduce a unified framework
This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, s…
In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting ta
Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized mul…
Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation…
Tactile sensing is important for contact-rich tasks especially in where an in-hand manipulation is involved. In teleoperation, such feedback can provide information of when and where the contacts happen, and is essential for a human operator to make appropriate actions. To improve the experience in
Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the
In robotic teleoperation, an operator usually needs to be trained for long hours. One of the factors leading to this steep learning curve is the quality of telepresence. This letter proposes a novel and user-friendly telepresence interface for manipulation. Visual information from different views is
Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the incorporation of tactile sensing into RL for contact-rich tasks lacks investigation. In this paper, we model a tactile s…
At present, there are many challenges for robotic systems to grasp in a dynamic and unstructured environment. The Robotic Grasping and Manipulation Competition (RGMC) aims to encourage researchers to focus on these challenges. The solution proposed in this letter was used to compete in the 2019 and
In this paper, we present a novel approach for surgical task segmentation. A segmentation policy learns the correlations between features and segmentation points from manually labeled data. The most correlated features and rules for segmenting them are identified and learned. These form a complete s…
This paper presents a vision-based learning-by-demonstration approach for multi-robot manipulation. With this method, a vision system is involved in both the task demonstration and reproduction stages, and the speed and accuracy of the task reproduction are adapted according to the context of the de…
This paper presents an intelligent sewing system for personalized stent graft manufacturing, a challenging sewing task that is currently performed manually. Inspired by medical suturing robots, we have adopted a single-sided sewing technique using a curved needle to perform the task of sewing stents…
A fundamental problem with dual-arm robotic control is to find the coordinated motion resolution under high kinematic redundancy and intrinsic constraints of each robot. To solve this problem, this paper presents a multi-tasking, co-operative control framework, in which potential task conflicts and…