6 accepted papers
Wire-harnessing tasks pose great challenges to be automated by the robot due to the complex dynamics and unpredictable behavior of the deformable wire. Traditional methods, often reliant on dual-robot arms or tactile sensing, face limitations in adaptability, cost, and scalability. This paper introd…
Grasp planning is essential for robots to execute dexterous tasks. Solving the optimal grasps for various objects online, however, is challenging due to the heavy computation load during exhaustive sampling, and the difficulties to consider task requirements. This paper proposes a framework to combi…
Grasp planning for multi-fingered hands is computationally expensive due to the joint-contact coupling, surface nonlinearities and high dimensionality, thus is generally not affordable for real-time implementations. Traditional planning methods by optimization, sampling or learning work well in plan…
Dexterous manipulation has broad applications in assembly lines, warehouses and agriculture. To perform large-scale manipulation tasks for various objects, a multi-fingered robotic hand sometimes has to sequentially adjust its grasping gestures, i.e. the finger gaits, to address the workspace limits…
To enhance the robotic manipulation of deformable objects, a robust state estimator is proposed to track the object configuration in real time. A Gaussian mixture model (GMM) is constructed to register the object nodes towards the noisy point cloud. To deal with occlusion, the coherent point drift (…
In the application of physical human-robot interaction (pHRI), the collaboration between human and robot can significantly improve the production efficiency through combination of the human's flexible intelligence and the robot's consistent performance. In this application, however, it is an importa…