[robotics-worldwide] [news] CfP Deadline Extension - NIPS Workshop "Autonomously Learning Robots",

Gerhard Neumann geri at robot-learning.de
Sun Oct 5 02:32:54 PDT 2014

Upon various requests, we will extend the deadline for submissions for 
the  workshop to October, 10th, 2014. For details, please see below.

NIPS 2014 WORKSHOP on "Autonomously Learning Robots"


== Quick Facts ==

Call For Papers:
Authors can submit a 2-6 pages paper that will be reviewed by the 
organization committee. The papers can present new work or give a 
summary of recent work of the author(s). All papers will be considered 
for the poster sessions. Out-standing long papers (4-6 pages) will also 
be considered for a 20 minutes oral presentation. Submissions should be 
send per email to autonomous.learning.robots at gmail.com with the prefix 

We are happy to announce that we can offer travel scholarships for 
students that want to attend the workshop. The scholarship will be at 
maximum 500€ (approx. 630$) per student and will be handed out to the 
best 10 student submissions. Please indicate in the email of your 
submission whether the first author is a student and you want to apply 
for the scholarship.

Important Dates:
* 1st Call for Papers: August, 26th, 2014
* Paper submission deadline: October, 10th, 2014 (23:59 PST)
* Paper acceptance notification: October, 27th, 2014
* Camera-ready deadline: November, 30th, 2014

Conference:    NIPS 2014 (http://nips.cc/Conferences/2014/)
Location:         Montreal, Canada
Homepage: http://www.ias.tu-darmstadt.de/Workshops/NIPS2014

     Gerhard Neumann ( http://www.ias.tu-darmstadt.de/Team/GerhardNeumann)
     Joelle Pineau (http://www.cs.mcgill.ca/~jpineau/ 
     Peter Auer (http://personal.unileoben.ac.at/auer/)
     Marc Toussaint (http://ipvs.informatik.uni-stuttgart.de/mlr/marc/)

    - More Autonomous Reinforcement Learning for Robotics
    - Autonomous Sub-Goal Extraction
    - Bayesian Parameter and Model Selection
    - Active Search and Autonomous Exploration
    - Autonomous Feature Extraction, Kernel Methods and Deep Learning 
for Robotics
    - Learning from Human Instructions, Inverse Reinforcement Learning 
and Preference Learning for Robotics
    - Generalization of Skills with Multi-Task Learning
    - Learning Forward Models and Efficient Model-Based Policy Search
    - Learning to Exploit the Structure of Control Tasks
    - Movement Primitives and Modular Control Architectures

== Abstract ==

To autonomously assist human beings, future robots have to autonomously 
learn a rich set of complex behaviors. So far, the role of machine 
learning in robotics has been limited to solve pre-specified 
sub-problems that occur in robotics and, in many cases, off-the-shelf 
machine learning methods. The approached problems are mostly 
homogeneous, e.g., learning a single type of movement is sufficient to 
solve the task, and do not reflect the complexities that are involved in 
solving real-world tasks.

In a real-world environment, learning is much more challenging than 
solving such homogeneous problems. The agent has to autonomously explore 
its environment and discover versatile behaviours that can be used to 
solve a multitude of different tasks throughout the future learning 
progress. It needs to determine when to reuse already known skills by 
adapting, sequencing or combining the learned behaviour and when to 
learn new behaviours.  To do so, it needs to autonomously decompose 
complex real-world tasks into simpler sub-tasks such that the learned 
solutions for these sub-tasks can be re-used in a new situation. It 
needs to form internal representations of its environment, which is 
possibly containing a large variety of different objects or also 
different agents, such as other robots or humans. Such internal 
representations also need to shape the structure of the used policy 
and/or the used value function of the algorithm, which need to be 
flexible enough such to capture the huge variability of tasks that can 
be encountered in the real world. Due to the multitude of possible 
tasks, it also cannot rely on a manually tuned reward function for each 
task, and, hence, it needs to find a more general representations for 
the reward function. Yet, an autonomous robot is likely to interact with 
one or more human operators that are typically experts in a certain 
task, but not necessarily experts in robotics. Hence, an autonomously 
learning robot also should make effective use of feedback that can be 
acquired from a human operator.
Typically, different types of instructions from the human are available, 
such as demonstrations and evaluative feedback in form of a continuous 
quality rating, a ranking between solutions or a set of preferences. In 
order to facilitate the learning problem, such additional human 
instructions should be used autonomously whenever available. Yet, the 
robot also needs to be able to reason about its competence to solve a 
task. If the robot thinks it has poor competence or the uncertainty of 
the competence is high, the robot should request more instructions from 
the human expert.

Most machine learning algorithms are missing these types of autonomy. 
They still rely on a large amount of engineering and fine-tuning from a 
human expert. The human typically needs to specify the representation of 
the reward-function, of the state, of the policy or of other internal 
representations used by the learning algorithms. Typically, the 
decomposition of complex tasks into sub-tasks is performed by the human 
expert and the parameters of such algorithms are fine tuned by hand. The 
algorithms typically learn from a pre-specified source of feedback and 
can not autonomously request more  instructions such as demonstrations, 
evaluative feedback or corrective actions. We belief that this lack of 
autonomy is one of the key reasons why robot learning could not be scaled to
more complex, real world tasks. Learning such tasks would require a huge 
amount of fine tuning which is very costly on real robot systems.

== Goal ==

In this workshop, we want to bring together people from the fields of 
robotics, reinforcement learning, active learning, representation 
learning and motor control. The goal in this multi-disciplinary workshop 
is to develop new ideas to increase the autonomy of current robot 
learning algorithms and to make their usage more practical for real 
world applications. In this context, among the questions which we intend 
to tackle are

More Autonomous Reinforcement Learning
- How can we automatically tune hyper-parameters of reinforcement 
learning algorithms such as learning and exploration rates?
- Can we find reinforcement learning algorithms that are less sensitive 
to the settings of their hyper-parameters and therefore, can be used for 
a multitude of tasks with the same parameter values?
- How can we efficiently generalize learned skills to new situations?
- Can we transfer the success of deep learning methods to robot learning?
- How do learn on several levels of abstractions and also identify 
useful abstractions?
- How can we identify useful elemental behaviours that can be used for a 
multitude of tasks?
- How do use RL on the raw sensory input without a hand-coded 
representation of the state?
- Can we learn forward models of the robot and its environment from high 
dimensional sensory data? How can these forward models be used 
effectively for model-based reinforcement learning?
- Can we autonomously decide when to learn value functions and when to 
use direct policy search?

Autonomous Exploration and Active Learning
- How can we autonomously explore the state space of the robot without 
the risk of breaking the robot?
- Can we use strategies for intrinsic motivation, such as artificial 
curiosity or empowerment, to autonomously acquire a rich set of 
behaviours that can be re-used in the future learning progress?
- How can we measure the competence of the agent as well as our 
certainty in this competence?
- Can we use active learning to acquire improve the quality of learned 
forward models as well as to probe the environment to gain more 
information about the state of the environment?

Autonomous Learning from Instructions
- Can we combine learning from demonstrations, inverse reinforcement 
learning and preference learning to make more effective use of human 
- How can we decide when to request new instructions from a human experts?
- How can we scale inverse reinforcement learning and preference 
learning to high dimensional continuous spaces?
- Can we use demonstrations and human preferences to identify relevant 
features from the high dimensional sensory input of the robot?

Autonomous Feature Extraction
- Can we use feature extraction techniques such as deep learning to find 
a general purpose feature representation that can be used for a 
multitude of tasks.
- Can recent advances for kernel based methods be scaled to 
reinforcement learning and policy search in high dimensional spaces?
- What are good priors to simplify the feature extraction problem?
- What are good features to represent the policy, the value function or 
the reward function? Can we find algorithms that extract features 
specialized for these representations?

== Format ==

The workshop is designed to be a platform for presentations and 
discussion including the invited speakers, oral presentations of paper 
submissions and poster submissions. The scope of the workshop includes 
all  all areas connected to autonomous robot learning, including 
reinforcement learning, exploration strategies, Bayesian learning for 
adjusting hyper-parameters, representation learning, structure learning 
and learning from human instructions. There will be a poster session 
where interested authors in the topic can present their recent work at 
the workshop. The authors have to submit a two page abstract which can 
present new work, or a summary of the recent work of the authors (6 
pages) or also present new ideas for the proposed topics. The workshop 
will consist of seven plenary invited talks (30 minutes each) and short 
talks from selected abstract submissions. All accepted posters will be 
presented at two poster sessions (min. 60 minutes each).

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