[robotics-worldwide] [meetings] Workshop on Learning and control for autonomous manipulation systems: the role of dimensionality reduction

Pietro Falco pietro.falco at tum.de
Fri May 26 11:24:45 PDT 2017

Dear colleagues,

We cordially invite you to attend the Full-Day Workshop "Learning and
control for autonomous manipulation systems: the role of dimensionality
reduction" . The Workshop will be held at ICRA 2017 on June 2, 2017 from
8:30 to 17:00 in Room 4311/4312. The  webpage is:

The purpose of the workshop is to portray the level of autonomy that
anthropomorphic robotic systems have reached today and to chart possible
paths towards improved manipulation capabilities by means of
self-adaptability to the environment. The workshop intends to spotlight how
autonomy depends on the ability to adapt to the environment by learning from
experience, and how, for this purpose, physical interaction is critical and
consequently smart design makes the difference. This workshop aims at
discussing the integration of learning, control and design aspects that
should not be separated in the complex problem of robotic manipulation.
Indeed, these aspects can interact and take advantage of one another being
inspired by the functioning, reasoning and physical resemblance of human
beings. Of course, in this contest the perception is involved in the process
and the integration of visual and tactile sensing is a crucial issue during
the interaction with the environment.

Some of the questions we will try to answer are:

-How human experience can help to develop new paradigm for anthropomorphic
devices control -How a synergistic approach can help to simplify modeling of
high degrees of freedom systems -How dimensionality reduction will help the
learning process -How vision and tactile information can be integrated in
the learning process and in control strategies


Topics of interest include but not limited to the following:

-Dimensionality reduction in anthropomorphic design: mechanical and motor
-synergies - Model of reduced dimensions learned from humans for control
simplification of high degrees of freedom devices -Synergy-based learning
and control strategies -Learning visual representations for
perception-action systems -Learning grasping and manipulation from tactile
information -Vision and force integration for autonomous control of
manipulation systems -Dynamic movement primitives -Imitation learning and
Reinforcement learning



Tamar Flash - Principles underlying dimension reduction and compositionality
in human movements and robotic implementations

Antonio Bicchi  - Designing and Using Hands with More Synergies

Aude Billard - Learning the dimensions that do not matter is important to
offer robustness in control

Dongheui Lee - Challenges in Kinesthetic and Teleoperation Teaching of
Manipulation Skills

Jan Babic - Complementary sensorimotor control during physical human-robot

Ken Goldberg - The New Wave in Robot Grasping

Matthew Howard - Learnt Redundancy Resolution and Constraints in Grasping

Sergey Levine - Deep Robotic Learning and Dexterous Manipulation

Jan Peters - Learning Low-Dimensional Skills



Dr. Fanny Ficuciello, University of Naples Federico II

Dr. Sylvain Calinon, Idiap Research Institute

Dr. Pietro Falco,  Technical University of Munich

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