Recurrent State Representation Learning with Robotic Priors
Learning an internal state based on the observation is an
important task in robotics. The sensor inputs are mostly high
dimensional and only a small subspace is important for the robot.
Previous work presented an unsupervised method training a neural net
with a loss composed of robotic priors which has been effective in a
markovian observation space . In this thesis, we try to extend this
method to work on non markovian observation spaces, and train a
recurrent network which should transfer the non markovian observations
into a internal state space which fulfill the markov property. For
this, we adapt the robotic prios to the new task and evaluate our
method in a new experimental setting. As a goal, the robot should be
able to solve a simple navigation task using only the learned state
more to: Recurrent State
Representation Learning with Robotic Priors 
Identification of Beneficial Morphological Computation on Soft
Compliance in soft hands can be both beneficial and
detrimental to functionality. Although recent work has shown the
benefits of compliance to object and environment geometry, there is
little work in identifying and avoiding the negative aspects of
compliance while controlling soft hands. However, a planner or a
feedback-controller that exploits compliance should avoid the regions
of detrimental morphological computation and guide the interactions to
the favorable ones.
Luckily recent work in simulation has shown promising results in
differentiating between beneficial/detrimental morphological
computations. The challenge ahead is to whether these results can be
transferred to real systems. Our lab's work in hand sensorization is a
possible tool in this path.
more to: Identification of
Beneficial Morphological Computation on Soft Hands
Sensorized In-Hand Manipulation 
Hand 2 is a highly compliant soft robotic hand. Its actuators
passively adapt their shape to different objects and the environment.
Even though the control of the pneumatic hand is relatively simple, it
is capable of complex in-hand manipulation.
The recent addition of liquid metal strain sensors
has created the opportunity to obtain better feedback about the
current state of the hand.
The goal of this thesis is to utilize this new sensor information to
make the execution of different in-hand manipulation tasks more
more to: Sensorized In-Hand