%Aigaion2 BibTeX export from Idiap Publications
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@PHDTHESIS{Jankowski_THESIS_2024,
         author = {Jankowski, Julius},
       keywords = {Contact-Rich Manipula- tion, model predictive control, Robust  Control, Stochastic Dynamics, stochastic optimization, Trajectory optimization, Underactuated Systems},
       projects = {Idiap},
          title = {A Stochastic Approach to Contact-rich Manipulation},
           year = {2024},
         school = {Ecole Polytechnique F{\'{e}}d{\'{e}}rale de Lausanne},
       abstract = {For robots to operate in unstructured environments, they are required to interact with
objects through contact. Those contacts may be used to push objects to the side,
deform objects, or manipulate objects in-hand. This thesis addresses the problem of
controlling robots to exploit contacts to manipulate objects. Being able to anticipate
the outcome of such physical interactions is essential for robots to gain true autonomy.

However, contact interactions are particularly challenging to reason over in model-
based control approaches due to the discontinuous nature of contacts. Moreover,

interacting with objects the robot has not interacted with before will naturally lead
to uncertainty in the prediction of contact dynamics. For instance, the robot can not
anticipate the mass distribution of an object before making contact, which requires
the robot to reason over possible outcomes before touching the object in a potentially
unfavorable or unsafe way.
Throughout this thesis, we formulate the problem of contact-rich manipulation with

a robot manipulator as a model-predictive control problem. We explore stochas-
tic optimization to plan for robot control trajectories in realtime. We show that the

stochasticity in the optimization process enables the algorithm to explore the space of
contacts without relying on local gradients or discretization of the contact space. We
furthermore study how uncertainties in the physical properties of the object propagate
through the contact dynamics and how the robot can actively reduce such uncertainty

by exploiting favorable contact modes and sequences. We integrate the above contribu-
tions into a planning and control framework for robots to manipulate objects through

contacts in realtime. The framework is evaluated in a series of robot experiments,
demonstrating robots autonomously performing dynamic hand-overs, push objects to
a moving target, play air hockey, and manipulate objects robustly using two arms in
the presence of uncertainty in the dynamics of the object.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/Jankowski_THESIS_2024.pdf}
}