• Corpus ID: 247025640

ReorientBot: Learning Object Reorientation for Specific-Posed Placement

@article{Wada2022ReorientBotLO,
  title={ReorientBot: Learning Object Reorientation for Specific-Posed Placement},
  author={Kentaro Wada and Stephen James and Andrew J. Davison},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.11092}
}
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 this as objects may not initially be oriented such that the robot can grasp and then immediately place them in a specific goal pose. In this work, we present a vision-based manipulation system, ReorientBot, which consists of 1) visual scene understanding with pose estimation and volumetric reconstruction using an… 

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References

SHOWING 1-10 OF 38 REFERENCES
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
TLDR
A system that integrates object-level mapping and learningbased motion planning to generate a motion that safely extracts occluded target objects from a pile, and shows that the observation fusion of poses and depth-sensing gives both better performance and robustness to the model.
Learning to Regrasp by Learning to Place
TLDR
A system for robots to take partial point clouds of an object and the supporting environment as inputs and output a sequence of pick-and-place operations to transform an initial object grasp pose to the desired object grasp poses is proposed.
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
TLDR
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, and achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks.
Rearrangement: A Challenge for Embodied AI
TLDR
A framework for research and evaluation in Embodied AI is described, based on a canonical task: Rearrangement, that can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings.
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
TLDR
The Transporter Network is proposed, a simple model architecture that rearranges deep features to infer spatial displacements from visual input - which can parameterize robot actions and learns faster and generalizes better than a variety of end-to-end baselines, including policies that use ground-truth object poses.
Task-Driven Perception and Manipulation for Constrained Placement of Unknown Objects
TLDR
This work deals with pick-and-constrained placement of objects without access to geometric models, and proposes an algorithmic framework, which performs manipulation planning simultaneously over a conservative and an optimistic estimate of the object’s volume.
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
TLDR
This work presents a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision and demonstrates a real- time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.
Solving Rubik's Cube with a Robot Hand
TLDR
It is demonstrated that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot, made possible by a novel algorithm, which is called automatic domain randomization (ADR), and a robot platform built for machine learning.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
TLDR
This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Towards Robust Product Packing with a Minimalistic End-Effector
TLDR
The overall approach is demonstrated to be robust to execution and perception errors, and to achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects.
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