[robotics-worldwide] [meetings] 2nd CfP: RSS 2016 Workshop Robot-Environment Interaction for Perception and Manipulation

Karol Hausman hausmankarol at gmail.com
Mon Apr 11 13:25:04 PDT 2016



RSS 2016 Workshop on

Robot-Environment Interaction for Perception and Manipulation:
Interactive Perception Meets Reinforcement Learning and Optimal Control


Website: http://rss16ip-rl-oc.robotics.usc.edu

Important Dates :

Submission deadline: 29 April 2016

Notification of acceptance: 6 May 2016

Workshop date: 19 June 2016


We invite interested researchers to submit abstracts to the RSS 2016
Workshop on Robot-Environment Interaction for Perception and Manipulation.


Robots operating in real-world environments must tackle complex perception
and manipulation tasks. Exploratory interaction with the environment is an
efficient or even necessary way to obtain task-relevant information. Such
information is contained in the way the sensor signals change in
correlation with the robot’s actions. This knowledge is critical for
understanding the state of the world (i.e, perception) and for learning how
to change this state (i.e., manipulation).

Interacting with the environment has been studied in several, mostly
disjoint communities: dual and optimal control, reinforcement learning, and
interactive perception. Interactive perception - leveraging a robot's
actions to obtain and exploit information about complex environments - has
usually relied on basic, highly-abstracted actions. In contrast, optimal
control and reinforcement learning find optimal policies in complex
manipulation and locomotion tasks but have often relied on simplifying
assumptions about the robot’s perception.

We believe that a tight coupling between manipulation and perception is
vital to addressing manipulation tasks in partially observable
environments. In particular, it allows gathering informative sensory
signals through complex interactions with the environment and will enable
accomplishing more perceptually-demanding manipulation tasks.


The goal of this workshop is to bring together researchers from the
interactive perception, optimal control, and reinforcement learning
communities to discuss the aforementioned limitations and common problems.
By combining these communities’ strengths, we hope to progress towards
robotic systems that operate in partially observable environments and
tackle tasks that are both perceptually demanding and require complex
policies. We are looking forward to discussing future trends and open


Pieter Abbeel (University of California Berkeley)
Jeannette Bohg (Max Planck Institute for Intelligent Systems)
Michael Koval(Carnegie Mellon University)
Justus Piater (University of Innsbruck)
Martin Riedmiller (Google DeepMind)
Russ Tedrake (Massachusetts Institute of Technology)


Participants are invited to submit abstracts related to key challenges in
robot-environment interaction, particularly focused on coupling perception
and manipulation. Topics include but are not limited to: (i) Interactive
Perception, (ii) Optimal Control, (iii) Reinforcement Learning.

We invite submissions in the form of extended abstracts (up to 2 pages)
following RSS formatting guidelines. The abstracts will be reviewed by the
organizers. Accepted contributions will be featured in a poster session and
will be included in the workshop proceedings, which will be available at
the workshop webpage (http://rss16ip-rl-oc.robotics.usc.edu/). We encourage
work-in-progress to be submitted and will take this into account in the
review process.

Submissions and questions should be directed to hausmankarol at gmail.com
before 29th of April 2016. Please include "2016 RSS poster submission" in
the subject of the email. Notifications of acceptance will be given by May
6th 2016.


June 19th, 8:30-12:00
Rackam Auditorium at the University of Michigan
Ann Arbor, Michigan


All information regarding the workshop will be published on the website
http://rss16ip-rl-oc.robotics.usc.edu/. Soon, the workshop schedule will be
posted there as well.


Karol Hausman, University of Southern California
Herke van Hoof, TU Darmstadt
Nikolay Atanasov, University of Pennsylvania
Roberto Martin-Martin, TU Berlin
Oliver Brock, TU Berlin

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