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Found 10 papers, 4 papers with code
ReorientBot: Learning Object Reorientation for Specific-Posed Placement
Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks.
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
We evaluate our methods using the YCB objects in both simulation and the real world, achieving safe object extraction from piles.
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
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 in continuous robotics domains.
Ranked #7 on
Robot Manipulation
on RLBench
Morning commute in congested urban rail transit system: A macroscopic model for equilibrium distribution of passenger arrivals
This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system.
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion.
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables.
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter
We present joint learning of instance and semantic segmentation for visible and occluded region masks.
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects
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.
Probabilistic 3D Multilabel Real-time Mapping for Multi-object Manipulation
Probabilistic 3D map has been applied to object segmentation with multiple camera viewpoints, however, conventional methods lack of real-time efficiency and functionality of multilabel object mapping.
3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map
Picking objects in a narrow space such as shelf bins is an important task for humanoid to extract target object from environment.
