5 accepted papers
In this letter, we present a novel dual-task, closed-loop, visual servoing-based active vision framework in an eye-in-hand configuration. The proposed active vision framework continuously drives the camera motion by coupling continuous Next-Best-View (NBV) planning and visual servo control within a
In this letter, we present a novel dual-task, closed-loop, visual servoing-based active vision framework in an eye-in-hand configuration. The proposed active vision framework continuously drives the camera motion by coupling continuous Next-Best-View (NBV) planning and visual servo control within a …
We present a novel framework for non-prehensile shape manipulation of deformable objects using Deep Reinforcement Learning. Unlike previous approaches that rely on grasping, our method employs a sequence of gentle pushing actions to deform objects into target shapes. We introduce a continuous parame
We present a novel vision-based, 6-DoF grasping framework based on Deep Reinforcement Learning (DRL) that is capable of directly synthesizing continuous 6-DoF actions in cartesian space. Our proposed approach uses visual observations from an eye-in-hand RGB-D camera, and we mitigate the sim-to-real…
Actuation delay poses a challenge for robotic arms and cranes. This is especially the case in dynamic environments where the robot arm or the objects it is trying to manipulate are moved by exogenous forces. In this paper, we consider the task of using a robotic arm to compensate for relative motion…