Page Content
Interactive Perception [1]
- © RBO
Action is a crucial part of the perceptual process. Humans interact
with their environment constantly to reveal information and use the
interaction to understand the consequences of their actions in the
world. Interactive Perception exploits these ideas to build autonomous
mobile manipulators that use interactions to perceive, understand and
manipulate their environment.
more to: Interactive
Perception [2]
Manipulation Planning [3]
- © Robotics
Brushing teeth, eating, opening a door: humans interact with the
environment countless times each day without thinking of it.
Manipulation planning requires the understanding of Object, Robot, and
Environment (ORE) interactions. We research the "ORE triangle" to
advance the field and shift the manipulation planning paradigm toward
new and exciting realms.
more to: Manipulation
Planning [4]
Soft Hands [5]
- © RBO
We design, build, and use soft robotic hands, like the RBO Hand 2.
Made from rubber and actuated pneumatically, they are highly compliant
and extremely robust.
With them we can reliably grasp objects with uncertain geometries and
positions, by offloading some perceptual difficulties to the
morphological computation of the hand itself.
more to: Soft Hands
[6]
Sensorization of Soft Hands and Actuators [7]
- © RBO
Soft robotic actuators introduce novel challenges for their
sensorization. Due to the high flexibility and compliance of soft
material robots, traditional approaches that rely on joint encoders
and torque measurements are no longer applicable. Instead, we develop
novel sensor technologies and research clever ways of combining sensor
measurements with computation to extract relevant feedback.
more to: Sensorization of Soft
Hands and Actuators [8]
In-Hand Manipulation [9]
- © Copyright??
Unlike most robot grippers, human hands are versatile manipulators:
they are soft, compliant, dexterous, and can be used for multiple
tasks at once. We want robotic hands to be similarly versatile.
Therefore, investigations of different feature encodings,
sample-efficient learning algorithms, appropriate simulation
environments and various kinds of hand sensorization are employed to
tackle the problem of In-Hand-Manipulation.
more to: In-Hand
Manipulation [10]
Protein Structure Prediction [11]
- © Robotics
What shape does a protein take when it folds? This questions have
preoccupied researchers for decades. We are building algorithms that
leverage experimental and evolutionary data to answer this question,
and predict the structures of proteins from their sequences.
more to: Protein Structure
Prediction [12]
Check our Youtube Channel
[13]
------ Links: ------
[1]
https://www.robotics.tu-berlin.de/menue/research/in
teractive_perception/parameter/en/font0/minhilfe/
[2]
https://www.robotics.tu-berlin.de/menue/research/in
teractive_perception/parameter/en/font0/minhilfe/
[3]
https://www.robotics.tu-berlin.de/menue/research/ma
nipulation_planning/parameter/en/font0/minhilfe/
[4]
https://www.robotics.tu-berlin.de/menue/research/ma
nipulation_planning/parameter/en/font0/minhilfe/
[5]
https://www.robotics.tu-berlin.de/menue/research/so
ft_hands/parameter/en/font0/minhilfe/?no_cache=1
[6]
https://www.robotics.tu-berlin.de/menue/research/so
ft_hands/parameter/en/font0/minhilfe/?no_cache=1
[7]
https://www.robotics.tu-berlin.de/menue/research/se
nsorization_of_soft_hands_and_actuators/parameter/en/fo
nt0/minhilfe/?no_cache=1
[8]
https://www.robotics.tu-berlin.de/menue/research/se
nsorization_of_soft_hands_and_actuators/parameter/en/fo
nt0/minhilfe/?no_cache=1
[9]
https://www.robotics.tu-berlin.de/menue/research/in
_hand_manipulation/parameter/en/font0/minhilfe/
[10]
https://www.robotics.tu-berlin.de/menue/research/i
n_hand_manipulation/parameter/en/font0/minhilfe/
[11]
https://www.robotics.tu-berlin.de/menue/research/p
rotein_structure_prediction/parameter/en/font0/minhilfe
/
[12]
https://www.robotics.tu-berlin.de/menue/research/p
rotein_structure_prediction/parameter/en/font0/minhilfe
/
[13]
http://www.youtube.com/user/rboTUBerlin
