9 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
Deep reinforcement learning (RL) often relies on simulators as abstract oracles to model interactions within complex environments. While differentiable simulators have recently emerged for multi-body robotic systems, they remain underutilized, despite their potential to provide richer information. T
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…
We investigate the active manipulation of objects using model-free and long-horizon DRL (Deep Reinforcement Learning) to achieve target shapes. Our proposed approach uses visual observations consisting of segmented images, to mitigate the sim-to-real gap. We address a long-horizon manipulation task…
An important aspect of robotic grasping is the ability to detect incipient slip based on real-time information through tactile sensors. In this paper, we propose to use Video Vision Transformers to detect the onset of slip in grasping scenarios. The dynamic nature of slip makes Video Vision Transfor
In this paper, we propose a novel approach for transferring a deep reinforcement learning (DRL) grasping agent from simulation to a real robot, without fine tuning in the real world. The approach utilises a CycleGAN to close the reality gap between the simulated and real environments, in a reverse r…
The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are…