Interactive Perception-Action-Learning for Modelling Objects

Project Details


Manipulating everyday objects without detailed prior models is still beyond the capabilities of existing
robots. This is due to many challenges posed by diverse types of objects: Manipulation requires
understanding and accurate model of physical properties of objects such as shape, mass, friction,
elasticity, etc. Many objects are deformable, articulated, or even organic with undefined shape (e.g.,
plants) such that a fixed model is insufficient. On top of this, objects may be difficult to perceive,
typically because of cluttered scenarios, or complex lighting and reflectance properties such as
specularity or partial transparency. Creating such rich representations of objects is beyond current
datasets and benchmarking practices used for grasping and manipulation. In this project we will
develop an automated interactive perception pipeline for building such rich digitization.
Short titleIPALM
Effective start/end date01/05/201930/04/2022


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