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Learning & Interaction Lab   (Italian Inst. of Tech.)

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Lab members

Sylvain Calinon (Ext. Collaborator)
Danilo Bruno (Postdoc)
Leonel Rozo (Postdoc)
João Silvério (PhD Student)
Milad Malekzadeh (PhD Student)

Alumni

Antonio Pistillo
Tohid Alizadeh
Davide De Tommaso

 

Programming-by-demonstration.org provides an open access to researchers in robot programming by demonstration to post announcements, links, publications and other resources.

The website is created and maintained by Sylvain Calinon.



Open PhD positions starting in 2014: Learning & Interaction Lab (OUTDATED)

August 15th, 2013

Application deadline: September 20, 2013 (11:59am CET).

Download the PDF version of this Call.

A number of PhD openings in machine learning and human-robot interaction are available at the Learning and Interaction Lab, Department of Advanced Robotics, Italian Institute of Technology (IIT). The positions are fully funded, start in January 2014 and last 3 years. IIT is an English-language research institute located in Genoa, Italy.

Full details of the Call, application procedure and links (do not miss the Tip&tricks page) can be found at: http://www.iit.it/phdschool.

Application requirements:

Strongly motivated candidates holding a Master degree in Computer Science / Engineering / Mathematics or other related fields are invited to apply. Applicants should ideally have a background in machine learning, robotics or human-robot interaction, with strong mathematics and computer programming skills (Matlab, C++ or equivalent).

Application procedure:

The application procedure is described here: http://www.iit.it/phdschool. Well before the deadline, the applicants should in addition send a detailed CV, statement of motivation, BSc and MSc transcripts, degree certificates and other support material such as reference letters to Dr Sylvain Calinon (sylvain.calinoniit.it), by mentioning one of the Research Themes below. Other Themes that would fit with the research activities of the lab are also welcome.

Proposed Research Themes for 2014: (listed in BIOENGINEERING AND ROBOTICS: Curriculum Advanced Robotics and Robot Design)

  • Theme 10 – Human-robot collaborative manipulation and coordination in bimanual tasks

The recent introduction of compliant robots into the robotics market has opened up a host of new human-centric research possibilities in robot learning. Because robots are no longer put behind fences (as in large manufacturing plants), they are increasingly capable of executing tasks in collaboration with human users. Such human-robot collaboration requires drastic changes in the way robots skills are represented, with the way they move, learn, react, and physically interact with the users and environment.    

The perspective is to go beyond reference trajectory tracking control by exploiting the active and/or intrinsic compliance of bimanual robots. With the fast development of these new robot technologies, one key element for robot learning by imitation and exploration is to encode the learned skills in compact probabilistic models. The aim is to guarantee generalization and adaptation, while handling the growth in the number of actuators and sensors. The principle of reducing the complexity of a nonlinear trajectory by encoding it as a superposition of simpler local motion elements (or movement primitives) will be extended to concepts such as impedance primitives or synergy primitives.

This PhD proposal will address research themes related to the problem of transferring bimanual collaborative manipulation skills to robots in a user-friendly manner. Such skills involve rich and diverse research questions such as passive/active roles switching, leader/follower behaviors, specialization, turn-taking, compliance, inter-agent synchronization, action anticipation, intention recognition in joint action and the use of non-verbal cues to communicate intent. These issues will be studied in two contexts: 1) with bidirectional social teaching interaction with the compliant humanoid robot COMAN; 2) within a manufacturing scenario with an innovative setup based on two 7 DOFs compliant manipulators with sensorized hands.

  • Theme 11 – Learning from demonstration in a soft robotic arm for assistance in minimally invasive surgery

This PhD proposal takes place within the STIFF-FLOP project (STIFFness controllable Flexible and Learn-able Manipulator for surgical OPerations), a project in collaboration with 11 universities, research institutes and companies in Europe. The aim is to transfer skills from a surgeon teleoperator to a flexible robot that can selectively stiffen its body to navigate within the patient through a trocar port. In minimally invasive surgery, tools go through narrow openings and manipulate soft organs that can move, deform, or change stiffness. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools.    

A soft robotic arm is developed within the project to manipulate objects while controlling the stiffness of selected body parts. This form of continuum robot is inspired by the way the octopus makes use of its embodiment to achieve skillful movements. This PhD proposal will focus on the learning and human-robot interaction aspects.

The proposal will address the problem of representing movements and reactive behaviors in continuum robots (or invertebrates), by handling both the continuity of the movement and the continuity of the robot with statistical dynamical systems. The objective is to extract relevant patterns from consecutive trials to learn force/position control manoeuvres so that the teleoperator can, over time, concentrate on high level decisions while the robot takes care of low level reactive manoeuvres. The learning problem will be studied in tight connection with the control problem to orchestrate the degrees of coupling of the flexible arm that best suit the statistics of the task (e.g., by stiffening the arm in task relevant dimensions).

  • Theme 12 – Bidirectional user interfaces for human-robot interaction

Research in robot learning can benefit from user interface technologies originally developed for mobile communication and video games applications. These developments can provide new ways of sensing the environment and interacting with the user in robotics, usually provided at low cost. Currently, a popular trend in this direction is to exploit structured light field of infrared beams (such as Kinect) and stereoscopic vision arrangement (such as Leap Motion) to measure depth information at various ranges of distance. In addition, other uprising technologies such as lightweight pico-projectors can be used to project information in the workspace shared by the user and the robot.    

While a fixed camera/projector system has a static field of view, embedding these devices at the tip of a robot opens up a host of new possible applications, in which the devices can move and project/detect from various viewpoints and angles. For example, the robot can actively reconfigure the sensing and projecting directions in regard to the task constraints, by projecting information on relevant objects or surfaces in the workspace, and by adapting the distance and orientation with respect to the specificities of the collaborative task and to the possible occlusion of the user, tools, etc. It also offers adaptive multiresolution tracking and projection capabilities (the detection of users in the robot surroundings requires a different field of view than the detection of precise positions of objects at reachable distance).

This PhD proposal will address the challenges of exploiting these new technologies as user-friendly interfaces to facilitate the communication and transfer of new skills to robots.


Open PhD positions starting in 2013: Learning & Interaction Lab (OUTDATED)

July 24th, 2012

Deadline: September 21, 2012

Download the PDF version of this call for open PhD positions.

The Learning and Interaction Lab, Department of Advanced Robotics, Italian Institute of Technology (IIT) has a number of PhD openings in the field of machine learning and human-robot interaction. The positions are fully funded, start in January 2013 and typically last 3 years. IIT is an English-language research institute located in Genoa, Italy, a seaside Mediterranean city set on the beautiful Italian Riviera, where the cost of living is much more affordable than many other European cities. International applications are encouraged and will receive logistic support with visa issues.

Application website: http://www.iit.it/en/openings.html
or http://www.studenti.unige.it/postlaurea/dottorati/xxviiiciclo/IITen

Application requirements:

Strongly-motivated candidates holding a Master degree in Computer Science / Engineering / Mathematics or other related fields are invited to apply. Applicants should ideally have a background in machine learning, robotics or human-robot interaction, with strong mathematical and computer programming skills (Matlab, C++ or equivalent).

Application procedure:

To apply please send a detailed CV, statement of motivation, BSc and MSc transcripts, degree certificates and other support material such as reference letters to Dr Sylvain Calinon (sylvain.calinoniit.it). The applicants also need to fill the online application procedure from the University of Genova: http://www.studenti.unige.it/postlaurea/dottorati/xxviiiciclo/IITen.

The following research topics are available:

STREAM 1: Machine Learning, Robot Control and Human-Robot Interaction
(Advanced Robotics – Prof. Darwin Caldwell)

  • Theme 3.2 – Dextrous manipulation learning with bimanual compliant robots

Robotic systems get increasingly complex with the fast development of new hardware and sensing technologies, not only with respect to the number of motors and sensors, but also with respect to the new actuation/perception modalities that will be endowed in Tomorrow’s robots. One such new perspective is to go beyond reference trajectory tracking control by exploiting active and/or intrinsic compliance capabilities of the robots. Such perspective requires us to redefine the machine learning problems towards a flexible regulation of stiffness and damping behaviors. With the fast development and expected widespread use of these new robot technologies, one key element for robot learning by imitation and exploration is to flexibly encode the learned skills with a minimum number of efficient control variables. The aim is to guarantee generalization and adaptation capabilities while avoiding to grow with the number of articulations or sensory modalities, in order to ensure real-time adaptive behavior.

The problem of bimanual coordination in such new settings needs to be thoroughly revisited. This PhD proposal will address research themes such as learning and adaptation of local sensory-motor activity couplings. The principle of reducing the complexity of a non-linear trajectory by representing it as a superposition of simple local motion elements (or movement primitives) will be extended to concepts such as impedance primitives or synergy primitives.

   

The role of haptics in dextrous manipulation skill acquisition will be explored in the context of bidirectional social teaching interaction with the compliant full humanoid robot COMAN, as well as in an industrial context with an innovative cooperative manufacturing setup based on two 7 DOFs compliant manipulators with sensorized hands.

  • Theme 3.3 – From human-human to human-robot collaborative skills acquisition

The recent introduction of robots with compliant capabilities into the robotics market has opened up a host of new, human-centric research possibilities, for scientists working in the fields of robot learning and social robotics. Two examples include kinesthetic teaching and human-robot cooperation. Because robots are no longer “put behind fences”, they are increasingly capable of executing tasks in collaboration with human users. Such human-robot collaboration requires engineers to make drastic changes in the way robots move, learn and interact with users. This PhD proposal addresses the problem of transferring collaborative manipulation skills to robots in a user-friendly manner. Such skills involve rich and diverse behaviors such as the assignment of leader/follower behaviors, passive/active roles switching, specialization, turn-taking, compliance, inter-agent synchronization, action anticipation, and the use of non-verbal cues to communicate intent.    

There are clear limitations to engineering solutions currently being used to implement such skills in robots. Critically, these skills sometimes appear to us as naturally grounded. It is proposed to study how versatile robotic peers could be developed, by looking at human-human collaboration to gain a better understanding of the mechanisms supporting the acquisition of collaborative manipulation skills.

The neurocognitive mechanisms supporting human-human cooperation will be studied in collaboration with Prof. Roger Newman-Norlund, Director of the Division of Motor Control and Rehabilitation at the University of South Carolina. The nature and roles of mutual responsiveness, complementary action, intention recognition and empathy in joint action will be studied from behavioral, psychological and cognitive neuroscience perspectives, by considering the performance of human-human dyads comprised of healthy subjects and subjects with impaired social abilities (i.e. Autism).

The human-robot cooperation experiments will be conducted with the compliant full humanoid robot COMAN, as well as with two 7 DOFs compliant manipulators with sensorized hands.

  • Theme 3.4 – Learning from demonstrations in a soft robotic arm for assistance in minimally invasive surgery

This PhD proposal takes place within the STIFF-FLOP project (STIFFness controllable Flexible and Learn-able Manipulator for surgical OPerations), which is a collaboration with 11 universities, research institutes and companies in Europe (KCL, UK, SSSA, Italy, TRI, Spain, PIAP, Poland, HUJI, Israel, UoS, UK, USiegen, Germany, Shadow, UK, FRK, Poland and EAES, Netherlands).

In minimally invasive surgery, tools go through narrow openings and manipulate soft organs that can move, deform, or change stiffness. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools. A soft robotic arm will be available within the project to manipulate objects while controlling the stiffness of selected body parts. This PhD proposal will focus on the learning, human-robot interaction and variable compliance manipulation aspects.

   

The objective is to exploit the relevant statistical information contained in multiple demonstrations from the teleoperator to learn force/position control manoeuvres so that the teleoperator can, over time, concentrate on high level decisions while the robot takes care of low level reactive control manoeuvres in a semi-autonomous fashion. The PhD candidate will conduct robotic experiments to answer a number of key questions in applied machine learning to control the stiffness of selected parts of the body, to move in a constrained space, and to exert desired forces on soft objects with uncertain impedance parameters.

Probabilistic models such as hidden Markov models (HMM) and Gaussian mixture regression (GMR) will be explored to learn control policies that take into account variability and correlation information collected by consecutive trials. The learning problem will be explored in tight connection with the control problem to orchestrate the degrees of coupling of the flexible arm that best suit the statistics of the task (e.g., by stiffening the arm in task relevant dimensions).


Call for Posters – IROS 2012 Workshop “Learning and Interaction in Haptic Robots”

May 11th, 2012

IROS 2012 Workshop on Learning and Interaction in Haptic Robots, Friday October 12, 2012
Location: Vilamoura, Algarve, Portugal

Organizers:
Dongheui Lee (Technische Universität München, Germany)
Sylvain Calinon (Italian Institute of Technology, Italy)

Motivation and Objectives:
Research on robot learning from demonstration has received great attention in the last decade since it can serve a useful methodology for intuitive robot programming, even by general users without robotics expertise. In the pioneering research investigations of this field, demonstrations were provided either by teleoperating the robot, or by vision/motion sensors recordings of the user doing the task. The recent hardware and software developments towards compliant and tactile robots are changing this picture. New solutions are now being offered to the user to physically interact with the robot to transfer or refine skills. This full day workshop will focus on the new robot learning perspectives that such new interaction modality offers.
Physical interaction in the context of robot learning is a young but promising upcoming research topic. It provides a natural interface to kinesthetic transfer of skills to the robot, where the user can demonstrate or refine the task in the robot’s environment while feeling its capabilities and limitations. With the new development of compliant controllers, backdrivable motors and artificial skins, new perspectives in learning arose by exploiting the natural teaching propensity of the user, already being familiar with social interaction such as scaffolding, molding or kinesthetic teaching. In this workshop, experts in machine learning, physical human robot interaction, and compliant robot control will introduce their research along this direction, sharing their views from different research perspectives, and discussing new challenges in this uprising field. Topics such as skill transfer, kinesthetic teaching interfaces, learning and prediction, compliant robot control, safe physical interaction, haptic and tactile guidance will be covered.

Topics:
• Skill transfer
• Kinesthetic teaching
• Haptic/tactile guidance
• Learning interaction/impedance control
• Learning by imitation
• Physical human robot interaction
• Machine learning for robot control, adaptive robot control
• Biological inspired principles for learning interaction

Invited speakers:
• Heni Ben Amor (Technische Universitaet Darmstadt, Germany)
• Aude Billard (EPFL, Switzerland)
• Etienne Burdet (Imperial College London, UK)
• Abderrahmane Kheddar, Andre Crosnier (CNRS-AIST JRL, Japan / LIRMM, France)
• Kazuhiro Kosuge (Tohoku University, Japan)
• Fabio Dalla Libera (Osaka University, Japan)
• Yoshihiko Nakamura (University of Tokyo, Japan)
• Pierre-Yves Oudeyer (INRIA Bordeaux, France)
• Jan Peters (Technische Universitaet Darmstadt, Germany)
• Gordon Cheng, Marcia Riley (Technische Universität München, Germany)
• Andrea Thomaz (Georgia Institute of Technology, USA)
• Sethu Vijayakumar (University of Edinburgh, UK)

Important Dates:
Submission Deadline: July 31, 2012
Notification of acceptance: August 24, 2012
Final paper Deadline: September 14, 2012

Submission:
Extended abstracts (1-2 pages) will be reviewed by the program committee members. Accepted contributions will be presented as posters. Submissions should be in pdf, formatted according to the IEEE conference templates and submitted via email to dhleetum.de and sylvain.calinoniit.it with the keyword “IROS12WS” in the subject line.

Support:
This workshop is supported by the FP7 European Project SAPHARI “Safe and Autonomous Physical Human-Aware Robot Interaction”.


2 open postdoc positions in robot learning by imitation and reinforcement at the Italian Institute of Technology (Outdated)

November 9th, 2011

Application deadline: December 4, 2011.

The Department of Advanced Robotics at the Italian Institute of Technology, an English-language research institute, has 2 postdoc openings in the area of machine learning applied to compliant robot control.

The successful candidates will participate in a new European research project called STIFF-FLOP (STIFFness controllable Flexible and Learn-able Manipulator for surgical OPerations) starting early 2012. The project is a collaboration of 11 universities, research institutes and companies in Europe: KCL (UK), SSSA (Italy), TRI (Spain), PIAP (Poland), HUJI (Israel), UoS (UK), USiegen (Germany), Shadow (UK), FRK (Poland) and EAES (Netherlands).

We are seeking highly motivated and talented candidates who wish to contribute to the development and experimental validation of novel imitation learning and reinforcement learning algorithms applied to a variety of compliant robots available in the Department of Advanced Robotics. These platforms include: a bimanual upper-torso composed of two Barrett WAM manipulators, a KUKA LightWeight Arm and the compliant humanoid robot COMAN.

The developed algorithms will be extended to the challenge of minimally invasive surgery, in which robotic tools must enter through narrow openings and manipulate soft organs that can move, deform, or change stiffness. The successful candidates will concentrate on the learning, collaborative human-robot interaction and/or variable compliance manipulation aspects of this project.

The research will be conducted within the Learning and Interaction Group. Contracts are up to 4 years with respect to the duration of the project. Salaries will depend on experience, with policies providing additional pension and health benefits. Applicants may also qualify for reduced tax benefits. Expected starting date is February 2012.

For further information please contact: Dr Sylvain Calinon (sylvain.calinoniit.it).

IIT is located in Genova, Italy, a seaside Mediterranean city set on the beautiful Italian Riviera, where the cost of living is much more affordable than many other European cities. International applications are encouraged and will receive logistic support with visa issues.

Application Requirements:

  • PhD degree in Computer Science, Mathematics or Engineering
  • High-quality publications record
  • Strong interest in machine learning
  • A high level of competence in one or more of the following areas: imitation learning, reinforcement learning, probabilistic models, MATLAB and C/C++ programming
  • Experience in robot control is a plus
  • Fluency in written and spoken English (IIT is an English language research institute)

 
Application Procedure:

To apply, please send a detailed CV, a statement of motivation, copies of degree certificates, grade transcripts, contact information of at least two references, and other support materials such as reference letters to Dr Sylvain Calinon (sylvain.calinoniit.it) and Dr Petar Kormushev (petar.kormusheviit.it), by quoting STIFF-FLOP in the email.


2 open postdoc positions in Machine Learning for Robot Control of Autonomous Underwater Vehicles (AUV) at the Italian Institute of Technology (Outdated)

November 9th, 2011

Deadline of application: December 4, 2011.

The Department of Advanced Robotics has 2 Post Doc openings in the research areas of Reinforcement learning and Imitation learning applied to robot control of Autonomous Underwater Vehicles (AUV).

The successful candidates will participate in a 3-year research project funded by the European Commission under the Seventh Framework Programme (FP7-ICT, STREP, Cognitive Systems and Robotics) called “PANDORA” (Persistent Autonomy through learNing, aDaptation, Observation and ReplAnning) which will start in January 2012.

The project is a collaboration of five universities and institutes in Europe: Heriot Watt University (UK), Italian Institute of Technology (Italy), University of Girona (Spain), King’s College London (UK), and National Technical University of Athens (Greece).

The accepted candidates will contribute to the development and experimental validation of novel reinforcement learning and imitation learning algorithms for specific application to robot control of autonomous underwater vehicles.

The research work includes conducting experiment with AUVs in water tanks in collaboration with the other project partners. The developed machine learning algorithms will also be applied for other robots available at IIT, such as the compliant humanoid robot COMAN, the humanoid robot iCub, Barrett WAM manipulator arm, and KUKA LWR arm robot.

The research will be conducted within the Learning and Interaction Group. The salary will depend on the candidate’s experience. The current range is from 30,000-40,000 Euros per year excluding additional pension and health benefits. Applicants may also qualify for reduced taxes benefits. Contracts are for up to 3 years. Expected starting date is February 2012.

International applications are encouraged and will receive logistic support with visa issues. For further information please contact: Dr Petar Kormushev (petar.kormusheviit.it).

Application Requirements:

  • PhD degree in Computer Science, Mathematics or Engineering
  • High-quality publication record
  • Strong interest in machine learning algorithms
  • Strong competencies in some of these areas: machine learning, reinforcement learning, imitation learning, MATLAB and C/C++ programming
  • Experience in robot control is a plus
  • Fluency in both spoken and written English

 
Application Procedure:

To apply please send a detailed CV, a statement of motivation, degree certificates, grade of transcripts, contact information of at least two references, and other support materials such as reference letters to: Dr Petar Kormushev (petar.kormusheviit.it) and Dr Sylvain Calinon (sylvain.calinoniit.it), quoting PANDORA in the email subject.


Open PhD positions at ADVR-IIT for 2012 (outdated)

August 25th, 2011

There are several open PhD positions at ADVR-IIT for 2012 (deadline for application September 23, 2011).

http://www.iit.it/en/resources/calls/unige/xxvii-cycle-doctoral-school-on-life-and-humanoid-technologies.html

For “Machine Learning, Robot Control and Human-Robot Interaction”, see Stream 3, Themes 2.7 and 2.8, p.22 on the program for Doctoral Course on Robotics, Cognition and Interaction Technologies in this PDF document.

Additionally, a PhD position opening in learning by imitation is advertised below (same instruction as above):

Theme 2.8b: Learning from demonstrations in a soft robotic arm for assistance in minimally invasive surgery

This PhD position will take place within a new European Project starting in 2012, in which the PhD candidate will collaborate with several universities and institutes in Europe. In minimally invasive surgery, tools go through narrow openings and manipulate soft organs that can move, deform, or change stiffness. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools. A soft robotic arm will be available within the project to manipulate objects while controlling the stiffness of selected body parts. This PhD proposal will focus on the learning, human-robot interaction and variable compliance manipulation aspects.

The objective is to use multiple demonstrations from the teleoperator to learn force/position control manoeuvres so that the teleoperator could, over time, concentrate on high level decisions while the robot takes care of low level reactive control manoeuvres in a semi-autonomous fashion. The PhD candidate will conduct robotic experiments to answer a number of questions in the areas of learning to control the stiffness of selected parts of the body, moving in a constrained space, and to exert desired forces on soft objects with uncertain impedance parameters.

Probabilistic encoding schemes based on Hidden Markov Models will be used to learn a policy that takes into account variability and correlation information collected during consecutive trials. The aim is to estimate an adequate level of compliance depending on the task requirements to leverage the operator with operations that are not directly relevant for the task. The use of novel control algorithms will be explored to arbitrate the degree of coupling of the flexible arm to best suit the statistics of the task (e.g., by stiffening the arm in task relevant dimensions).


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