Autonomous robotic stone stacking with online next best object target pose planning

By Fadri Furrer, Martin Wermelinger, Hironori Yoshida, Fabio Gramazio, Matthias Daniel Kohler, Roland Yves Siegwart, Marco Hutter. 2017

73 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Building and Construction, Mechanical Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Abhinav Kumar Gupta
2. Sergey Levine
3. Andrea Vedaldi
4. Dhiraj Gandhi
5. Lerrel Pinto
6. Shubham Tulsiani
7. Pieter Abbeel
8. Ingmar Posner

In Building and Construction

1. Pieter Abbeel

In Mechanical Engineering

1. Pieter Abbeel
2. Abhinav Kumar Gupta
3. Ingmar Posner
4. Dhiraj Gandhi
5. Lerrel Pinto

Citations in the corpus

• Visual Imitation Made Easy (2020, methodology): “To study the effectiveness of this tool, we focus on two challenging tasks: (a) non-prehensile pushing [12, 13], and (b) prehensile stacking [14, 15].”
• ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking (2018, methodology): “More recently, advances in 3D perception and physical simulation have been exploited to stack irregular objects like stones [8].”