107 citations · 529 across the 30 of their papers we have counts for
7 papers · 1 filter
Grasp that optimises objectives along post-grasp trajectories
Amir M Ghalamzan E, Nikos Mavrakis, Rustam Stolkin
In this article, we study the problem of selecting a grasping pose on the surface of an object to be manipulated by considering three post-grasp objectives. These objectives includ…
Dense RGB-D semantic mapping with Pixel-Voxel neural network
Cheng Zhao, Li Sun, Pulak Purkait +1
For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene's geometrical features…
Safe Robotic Grasping: Minimum Impact-Force Grasp Selection
Nikos Mavrakis, Amir M. Ghalamzan E., Rustam Stolkin
This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped…
Human-in-the-loop optimisation: mixed initiative grasping for optimally facilitating post-grasp manipulative actions
Amir M. Ghalamzan Esfahani, Firas Abi-Farraj, Paolo Robuffo Giordano +1
This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a h…
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
Li Sun, Gerardo Aragon-Camarasa, Simon Rogers +2
This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Di…
Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data
Li Sun, Cheng Zhao, Rustam Stolkin
This paper addresses the problem of RGBD object recognition in real-world applications, where large amounts of annotated training data are typically unavailable. To overcome this p…
A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition
Cheng Zhao, Li Sun, Rustam Stolkin
This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.…