[robotics-worldwide] [meetings] Call for Participation: Policy Representation for Humanoid Robots, HUMANOIDS 2014

Neil T. Dantam ntd at rice.edu
Wed Nov 12 13:06:58 PST 2014

Call for Participation

*                 IEEE Humanoids 2014                   *
* Workshop on Policy Representation for Humanoid Robots *

Key Facts

Humanoids 2014 Workshop, November 18, 2014
Location: Madrid, Spain, HOTEL MELIA CASTILLA 4, Room Junta 1

URL: http://www.neil.dantam.name/2014/humanoids-policy-workshop/


Neil T. Dantam, Gerhard Neumann, Heni Ben Amor


Autonomous robots must handle changes in the environment,
uncertainties, and faults. They require not just predetermined plans
for a single execution path, but instead, policies describing online
response under varying conditions -- a set of execution paths. The
greater the scope of the policy, the greater the capabilities and
reliability of the robot. There are a variety of policy techniques and
system types: model-based and learned policies, continuous and
discrete state, deterministic and stochastic systems. The goal of this
workshop is to highlight recent applications of control policies for
solving complex humanoid robot tasks, as well as comparing existing
policy representations. We hope to identify policy representations
that are particularly well suited for controlling humanoid robots. We
will compare techniques for policy specification and representation,
looking for a common ground to combine assumedly disparate approaches
for autonomous capability and reliability. We strongly believe that
Humanoids 2014 is an ideal venue to shed light on recent successes and
next steps in policy representations for humanoid robots. From this
workshop, we expect participating researchers to identify and address
important challenges, techniques, and benchmarks necessary for complex
robot policy generation.


We hope to bring together outstanding researchers and graduate
students to discuss current trends, problems, and opportunities in
representing humanoid robot policies, encouraging communication and
common practices among scientists in this field. A key goal of the
workshop is to reconcile and integrate various different approaches
for policy representation. This will open new avenues to build more
comprehensive and autonomous robots. Given these insights, we want to
discuss important next steps and open problems in policy
generation. The invited speakers are renowned experts on robot
planning, control, and learning. Hence, they can contribute to a
better understanding of both the global overview of the field as well
as in-depth knowledge for their respective specialities. Researchers
will also have the opportunity to present their most recent results
and get feedback from fellow scientists and experts in the field. The
workshop is intended as an open platform for sharing and discussing
new ideas and open problems. Although robot policy representation is
the focal point of the workshop, the goal is to present this approach
in an accessible way. In this manner, we hope to familiarize members
of the audience who are new to this topic with its central concepts.

Invited Speakers
- Aaron Ames, Texas A&M
- Guilherme Maeda, TU Darmstadt
- Marc Deisenroth, Imperial College London
- Matt Zucker, Swarthmore


- Present recent advances in using control policies for solving
    difficult humanoid robot control tasks.

- Compare stochastic and deterministic policies, as well as
    time-dependent vs. state-dependent policies for humanoid robots.

- Discuss how feedback, e.g., proprioceptive feedback or
    high-dimensional vision data, is integrated into policies.

- Relate different approaches for policies, such as automata, temporal
    logics, motion primitives, hybrid systems models, Markov decision
    processes, neural networks, and reward functions.

- Discuss which policy representations are best suited for robot
    learning and automatic adaptation.

- Discuss policy representation challenges specific to humanoid robots
    such as dynamic stability and many degrees of freedom.

- Discuss and compare approaches to represent and handle uncertainty
    such as Bayesian approaches, intervals, alternative events, and
    fuzzy logic.

- Compare trade-offs between explicitly modeled and implicitly learned

- Discuss policy robustness in the presence of unmodelled conditions:
    the "unknown unknowns" problem.

- Investigate continuous versus discrete state policies for humanoid

- Compare human-centric issues, such as ease of policy specification,
    with robot-centric issues such as computational efficiency and
    representational compactness.

- Discuss which variables should be controlled, e.g. positions,
    velocities, torques, impedance.

- Investigate the application tasks of reactive versus open loop

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