Topics of Discussion

In this workshop, we would like to discuss the critical role of open-ended learning in object perception and grasp affordance. We invite a number of renowned experts in the field who will highlight the current successes and future challenges these robots face. In particular, we will discuss the current and future challenges and opportunities for open-ended approaches in this workshop by considering the following questions:

  1. What can be transferred from human human cognition to cognitive robotics?
  2. How human cognition translates to open-ended learning approach in robotics?
  3. What challenges, opportunities the open-ended approaches provide for incremental robot learning and learning from observations?
  4. What would be the target for open-ended approaches? To what extend open-ended approaches must generalise?
  5. How a robot (supervised or unsupervised) should incrementally collect data of its own experiences during interaction with objects?
  6. How object perception (cognition) can be extended to provide generative model of a scene (probabilistic scene descriptions)? And How this can be used for task informed grasping?
  7. How to evaluate performance of open-ended learning approaches? What are the right metrics to do so?
  8. What would be the right benchmarks, datasets that helps evaluate approaches and compare progress in this field?

Topics of Interest

Topics of interest include but not limited to the following:

  • Architectures for open-ended learning
  • Transfer learning from one to another type of robot hand
  • Open-ended grasping of deformable objects
  • Lifelong learning and adaptation for autonomous robots
  • Cognitive robotics
  • Deep learning for task-informed grasping
  • Deep transfer learning for object perception
  • Knowledge transfer and avoidance of catastrophic forgetting
  • Affordance learning and task informed grasping
  • Challenges of Human-Robot collaborative manipulation
  • Grasping and object manipulation
  • 3D object category learning and recognition
  • Active perception and scene interpretation
  • Coupling between object perception and manipulation
  • Learning from demonstrations

Great line-up of speakers

Henrik I. Christensen (Qualcomm)

Prof. Henrik I. Christensen is the Qualcomm Chancellor's Chair of Robot Systems and a Professor of Computer Science at Dept. of Computer Science and Engineering UC San Diego. He is also the director of the Institute for Contextual Robotics. His research has a strong emphasis on "real problems with real solutions".

*Topic: Robust grasp preimages under unknown mass and friction distributions.

Tamim Asfour (KIT)

Tamim Asfour is full Professor at the Institute for Anthropomatics and Robotics, where he holds the chair of Humanoid Robotics Systems and is head of the High Performance Humanoid Technologies Lab (H2T). His current research interest is high performance 24/7 humanoid robotics.

*Topic: Current Successes and Future Challenges in Humanoid Grasping and Manipulation in the Real World.

Serena Ivaldi (INRIA)

Serena is a tenured research scientist in INRIA Nancy Grand-Est (France), working in the project-team LARSEN. Serena is currently focused on robots collaborating with humans. She is interested into combining ML with control to improve the prediction and interaction skills of robots.

*Topic: Teaching a Robot to Grasp Irregular Objects with Machine Learning and Human-in-the-Loop Approaches.

Hao Su (UC San Diego)

Hao Su has been in UC San Diego as Assistant Professor of Computer Science and Engineering since July 2017. He is affiliated with the Contextual Robotics Institute and Center for Visual Computing. He served on the program committee of multiple conferences and workshops on computer vision, computer graphics, and machine learning. He is the Area Chair of CVPR'19.

*Topic: Semantic Scene Segmentation using PartNet Models.

Luis Seabra Lopes (Uni. of Aveiro)

Luis Seabra Lopes is Associate Professor of Informatics in the Department of Electronics, Telecommunications and Informatics of the University of Aveiro, Portugal. He received a PhD in Robotics and Integrated Manufacturing from the New University of Lisbon, Portugal, in 1998. Luís Seabra Lopes has long standing interests in robot learning, cognitive robotic architectures, and human-robot interaction.

*Topic: Interactive Open-Ended Learning Approaches for 3D Object Recognition.

Yukie Nagai (Uni. of Tokyo)

Yukie Nagai has been investigating underlying neural mechanisms for social cognitive development by means of computational approach. She designs neural network models for robots to learn to acquire cognitive functions such as self-other cognition, estimation of others’ intention and emotion, altruism, and so on based on her theory of predictive learning.

*Topic: Cognitive Development Based on Sensorimotor Predictive Learning..

Shuran Song (Columbia Uni.)

Shuran Song will be joining the School of Computing Science at Columbia University in New York, as an Assistant Professor in 2019. She earned her Ph.D. degree in Computer Science at Princeton University in 2018. During her Ph.D., she spent time working at Microsoft and Google. Her research interests lie at the intersection of computer vision, computer graphics, and robotics.

*Topic: Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping.

Carlos Celemin Paez (Delft Uni.)

Carlos is a Postdoctoral researcher in the Cognitive Robotics Department at Delft University of Technology, in the group of Learning and Autonomous Control. His research is focused on Machine Learning for robot control, combining Reinforcement learning and human feedback in order to obtain data efficient methods which make feasible to learn directly on real systems.

*Topic: Teaching Robots Interactively from few corrections: Learning Policies and Objectives.

Julian Ibarz (Google AI)

Julian Ibarz is a Staff Software Engineer at Google AI. He is a technical lead within the Google Brain Robotics team and work on making robots smarter with deep reinforcement learning techniques. Prior to that, he worked 5 years in the Google Maps team helping automating mapping using deep learning.

*Topic: Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping; and Grasp2Vec: Learning Object Representations from Self-Supervised Grasping.

Luca Carlone (MIT)

Luca Carlone is the Charles Stark Draper Assistant Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, and a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS). He received his PhD from the Polytechnic University of Turin in 2012. His research interests include nonlinear estimation, numerical and distributed optimization, and probabilistic inference, applied to sensing, perception, and decision-making in single and multi-robot systems.

*Topic: It kinda works! Challenges and Opportunities for Robot Perception in the Deep Learning Era.

Program (tentative)

Time Speaker Topic
9:00-9:10 Hamidreza Kasaei Introduce the workshop and its goals
9:10-9:50 Henrik I. Christensen Robust grasp preimages under unknown mass and friction distributions.
9:50-10:25 Tamim Asfour Current Successes and Future Challenges in Humanoid Grasping and Manipulation in the Real World.
10:25-11:00 Serena Ivaldi Teaching a Robot to Grasp Irregular Objects with Machine Learning and Human-in-the-Loop Approaches.
11:00-11:30 Coffee break Interactive poster presentations
11:30-12:00 Hao Su Semantic Scene Segmentation using PartNet Models.
12:00-12:30 Luis Seabra Lopes Interactive Open-Ended Learning Approaches for 3D Object Recognition.
12:30-13:30 Paper presentations
and poster presentations
2 mins highlight poster presentations
followed by 46 mins interactive poster session
13:30-14:30 Lunch --
14:30-15:00 Shuran Song Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping.
15:00-15:30 Carlos Celemin Paez
and Jens Kober
Teaching Robots Interactively from few corrections: Learning Policies and Objectives.
15:30-16:00 Yukie Nagai Cognitive Development Based on Sensorimotor Predictive Learning.
16:00-16:30 Julian Ibarz
and Vincent Vanhoucke
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping;
Grasp2Vec: Learning Object Representations from Self-Supervised Grasping.
16:30-17:00 Coffee break Interactive poster presentations
17:00-17:30 Luca Carlone It kinda works! Challenges and Opportunities for Robot Perception in the Deep Learning Era.
17:30-18:30 Panel Discussion Panel Discussion
18:30-18:35 End

Call for Papers

Submissions

We encourage contributions with either a contributed paper ( IEEE conference format, 6 pages without references), an extended abstract of a published work (IEEE conference format, 2 pages maximum).
All papers are reviewed using a blind review process. Authors of selected contributed papers may be asked to submit extended versions of their papers for an RA-L special issue. All papers must be written in English and submitted electronically in PDF format by emailing it to cognitiverobotic@gmail.com

Important Dates

  • Submission Deadline: 20 September 2019
  • Notification: 1 October 2019
  • Workshop Date: 8 November 2019






Organizers

Dr. Hamidreza Kasaei, University of Groningen, the Netherlands

Hamidreza joined the Department of Artificial Intelligence of the University of Groningen, the Netherlands, as a Faculty of Science and Engineering (FSE) Fellow in 2018. Prior to joining University of Groningen, he finished his Ph.D. on open-ended learning approaches to recognise multiple objects and their grasp affordances concurrently as part of an FP7 project named RACE: Robustness by Autonomous Competence Enhancement. His main research interests lie in the area of 3D Object Perception, Grasp Affordance, and Object Manipulation.

Dr. Amir Ghalamzan Esfahani, University of Lincoln, UK

Amir is a Senior Lecture at the University of Lincoln (UoL), UK. He is also a member of Lincoln Centre of Autonomous Systems (LCAS) at UoL. Prior to joining UoL, he was a Research Fellow at University of Birmingham, UK, conducteding research on robotic grasping and manipulation.