[robotics-worldwide] [meetings] CfP: NIPS 2018 Workshop on Imitation Learning and its Challenges in Robotics

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[robotics-worldwide] [meetings] CfP: NIPS 2018 Workshop on Imitation Learning and its Challenges in Robotics

Mustafa Mukadam
Call for Abstracts

NIPS 2018 Workshop on
Imitation Learning and its Challenges in Robotics

Location: Montreal, Canada
Date: Dec 7, 2018
URL: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_view_nips18-2Dilr&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=dMVaGcsvj7cwK5rAqGnmLYg4C1AlU0XAFLLXTCDI1bw&s=eVL5Cle78nwPKFFjW_uOyASnJzN5K1yu3NzMsGcRSns&e=

Important Dates
Oct 19 - Submission deadline (AoE time)
Oct 29 - Notification of acceptance
Nov 16 - Camera ready deadline
Dec 7 - Workshop

Many animals including humans have the ability to acquire skills,
knowledge, and social cues from a very young age. This ability to imitate
by learning from demonstrations has inspired research across many
disciplines like anthropology, neuroscience, psychology, and artificial
intelligence. In AI, imitation learning (IL) serves as an essential tool
for learning skills that are difficult to program by hand. The
applicability of IL to robotics in particular, is useful when learning by
trial and error (reinforcement learning) can be hazardous in the real
world. Despite the many recent breakthroughs in IL, in the context of
robotics there are several challenges to be addressed if robots are to
operate freely and interact with humans in the real world.

Some important challenges include: 1) achieving good generalization and
sample efficiency when the user can only provide a limited number of
demonstrations with little to no feedback; 2) learning safe behaviors in
human environments that require the least user intervention in terms of
safety overrides without being overly conservative; and 3) leveraging data
from multiple sources, including non-human sources, since limitations in
hardware interfaces can often lead to poor quality demonstrations.

In this workshop, we aim to bring together researchers and experts in
robotics, imitation and reinforcement learning, deep learning, and human
robot interaction to
- Formalize the representations and primary challenges in IL as they
pertain to robotics
- Delineate the key strengths and limitations of existing approaches with
respect to these challenges
- Establish common baselines, metrics, and benchmarks, and identify open

We solicit up to 4 pages extended abstracts (excluding references)
conforming to the NIPS style. Submissions can include archived or
previously accepted work (please make a note of this in the submission).
Reviewing will be single blind.

Submission link: https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dnips18ilr&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=dMVaGcsvj7cwK5rAqGnmLYg4C1AlU0XAFLLXTCDI1bw&s=4Q96IfPeFm9kDd136gt8oBkV2XwcfF67Q8nsO0P_EqA&e=

Topics of interest include, but are not limited to:
- Sample efficiency in imitation learning
- Learning from high dimensional demonstrations
- Learning from observations
- Learning with minimal demonstrator effort
- Few shot imitation learning
- Risk aware imitation learning
- Learning to gain user trust
- Learning from multi modal demonstrations
- Learning with imperfect demonstrations

All accepted contributions will be presented in interactive poster
sessions. A subset of accepted contributions will be featured in the
workshop as spotlight presentations.

Mustafa Mukadam (Georgia Tech)
Sanjiban Choudhury (University of Washington)
Siddhartha Srinivasa (University of Washington)

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