Overview

The workshop will be held on 4st November 2019 in The Venetian Macao, Macau, China in the context of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), one of the largest and highest impact robotics research conferences worldwide.

Manipulating objects autonomously and in unstructured environments is one of the basic skills for robots to support people during everyday life outside industrial cages. The study of autonomous manipulation in robotics aims at transferring human-like perceptive skills to robots so that, combined with state of the art control techniques, they could be able to achieve similar performance in manipulating objects.

The great complexity of this task makes autonomous manipulation one of the open problems in robotics that has been drawing a big interest in the community in the recent years. Conventional approaches attempt to reconstruct the scene using 3D vision and compute agrasping pose that attain force closure constraints, or by querying a database of precomputed or learned poses. More recently, grasping has been addressed using end-to-end learning methods showing great performance. However, these methods require robots to perform thousands of trials. For this reason their application is often focused on simple grippers and scenarios in which images are acquired from a top down view. Manipulation with multi finger hands and mobile robots is unfortunately still out of the scope of these techniques due to the problem complexity.


Objectives

The aim of this workshop is to discuss and present different techniques proposed for addressing the same problem: object manipulation.

More than a comparison, this workshop is designed to encourage people belonging to different research fields such as robotics and deep learning to share their approaches, ideas and problems regarding autonomous manipulation.

The workshop talks are grouped in three Sections according to the methodology used by the speaker research team to address autonomous manipulation:


Topics of interest

The following list provides a set of topics (keywords) addressed in the workshop.

 


Workshop Program

Time Activity
9:00 - 9:15 Introduction
Section 1 Learning-free manipulation
09:15 – 09:45 Speaker 1: Lorenzo Natale
09:45 – 10:15 Speaker 2: Oliver Brock
10:15 – 10:45 Poster Teasers/ Student papers presentations
10:45 – 11:15 Coffee Break: Poster session
Section 2 Supervised and imitation Learning for manipulation
11:15 – 11:45 Speaker 3: Tamim Asfour
11:45 – 12:15 Speaker 4: Robert Platt
12:15 – 12:45 Speaker 5: Marcus Vincze
13:00 – 14:00 Lunch Break
Section 3 Deep Reinforcement Learning for manipulation
14:15 – 14:45 Speaker 8: Juxi Leitner
14:45 – 15:15 Speaker 9: Abhishek Gupta
15:15 – 15:45 Speaker 10: Franziska Meier
Section 4 Open discussion (across fields)
15:45 – 16:15 Coffee Break: Poster session
16:15 – 18:00 Round-table

Invited Speakers

Lorenzo Natale, Istituto Italiano di Tecnologia (IIT)

Lorenzo Natale is Tenured Senior Researcher at the Italian Institute of Technology. He received his degree in Electronic Engineering (with honours) and Ph.D. in Robotics from the University of Genoa. He was later postdoctoral researcher at the MIT Computer Science and Artificial Intelligence Laboratory. He was invited professor at the University of Genova where he taught the courses of Natural and Artificial Systems and Antropomorphic Robotics for students.

Talk title

Grasping of unknown objects or objects whose pose is uncertain is still an open problem in robotics. The missing or noisy information on object models and poses strongly affects manipulation performance. On the other hand research on grasping is made difficult by the fact that research there are no methodologies for comparing results obtained using different robotic platforms. In the past few years we have developed a framework for grasping unknown objects of various shapes using superquadric models. We initially proposed to model objects using single superquadric function, and more recently, extended this approach to use multi-superquadrics to model objects with finer details. In the first part of this talk I will revise our work, showing experiments with the iCub humanoid robot on the YCB dataset. In the second part of the talk I will describe a benchmarking protocol and software called GRASPA, which is specifically devised to test effectiveness of grasp planners on real robots, proposing various metrics to take into account of features and limits of the specific platform.

Oliver Brock, TU Berlin

Oliver Brock is the Alexander-von-Humboldt Professor of Robotics in the School of Electrical Engineering and Computer Science at the Technische Universität Berlin in Germany, which is a German University of Excellence. He received his Ph.D. from Stanford University in the year 2000 and held post-doctoral positions at Rice University and Stanford University. He was an Assistant Professor and Associate Professor in the Department of Computer Science at the University of Massachusetts Amherst, before to moving back to the Technische Universität Berlin in 2009. The research of Brock's lab, the Robotics and Biology Laboratory, focuses on robot intelligence, mobile manipulation, interactive perception, grasping, manipulation, soft material robotics, interactive machine learning, deep learning, motion generation, and the application of algorithms and concepts from robotics to computational problems in structural molecular biology. Oliver Brock is the coordinator of the Research Center of Excellence "Science of Intelligence". He is an IEEE Fellow and was president of the Robotics: Science and Systems Foundation from 2012 until 2019.

Talk title

Talk abstract.

Tamim Asfour, Karlsruhe Institute of technology (KIT)

Speaker's bio.

Talk title

Talk abstract.

Robert Platt, Northeastern Universit

Speaker's bio.

Talk title

Talk abstract.

Markus Vincze, TU Wien

Speaker's bio.

Detecting and Handling Objects for Future Service and Industrial Robots

In the near future robots will operate more and more next to humans. Robots will be expected to know about all the objects in the domain where they are working, homes as well as industry. This will require methods to rapidly learn new objects, recognise and manipulate them. The talk will review recent advances such as learning objects from CAD models, learning object relations and parts, the use of semantic knowledge related to objects, and the detection of learned object classes from mobile robots.

Juxi Leitner, Australian Centre of Excellence for Robotic Vision (ACRV)

Speaker's bio.

Talk title

Talk abstract.

Abhishek Gupta, Berkeley Artificial Intelligence Research (BAIR) Lab, UC Berkeley

Speaker's Bio.

Talk title

Talk abstract.

Franziska Meier, Facebook AI Research

Speaker's bio.

Talk title

Talk abstract.


Call for Papers

Information for Authors

All submissions must be in PDF format, following the IEEE conference style in two-columns and be limited to 2 pages, including references and appendix.

All submissions will be peer-reviewed. Accepted papers will be presented during the workshop in a poster session. A number of selected papers will be presented as oral presentations or spotlight talks.

Send your PDF manuscript by email, with the subject including the string [MANIPULATION IROS 2019], to the following emails:


Important Dates


© 2019 Different Approaches, the Same Goal: Autonomout Object Manipulation