12 citations · 28 across the 8 of their papers we have counts for
9 papers
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition…
ReorientBot: Learning Object Reorientation for Specific-Posed Placement
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in t…
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Stephen James, Kentaro Wada, Tristan Laidlow +1
We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods…
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
Kentaro Wada, Edgar Sucar, Stephen James +2
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized preci…
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
Edgar Sucar, Kentaro Wada, Andrew Davison
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-c…
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter
Kentaro Wada, Kei Okada, Masayuki Inaba
We present joint learning of instance and semantic segmentation for visible and occluded region masks. Sharing the feature extractor with instance occlusion segmentation, we introd…
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects
Kentaro Wada, Shingo Kitagawa, Kei Okada +1
We present a robotic system for picking a target from a pile of objects that is capable of finding and grasping the target object by removing obstacles in the appropriate order. Th…
Probabilistic 3D Multilabel Real-time Mapping for Multi-object Manipulation
Kentaro Wada, Kei Okada, Masayuki Inaba
Probabilistic 3D map has been applied to object segmentation with multiple camera viewpoints, however, conventional methods lack of real-time efficiency and functionality of multil…
3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map
Kentaro Wada, Masaki Murooka, Kei Okada +1
Picking objects in a narrow space such as shelf bins is an important task for humanoid to extract target object from environment. In those situations, however, there are many occlu…