Fachgebiet Robotics

5 Items

Recent Submissions
Exploitation of environmental constraints in human and robotic grasping

Eppner, Clemens ; Deimel, Raphael ; Álvarez-Ruiz, José ; Maertens, Marianne ; Brock, Oliver (2015)

We investigate the premise that robust grasping performance is enabled by exploiting constraints present in the environment. These constraints, leveraged through motion in contact, counteract uncertainty in state variables relevant to grasp success. Given this premise, grasping becomes a process of successive exploitation of environmental constraints, until a successful grasp has been establish...

Special Issue on the Sixteenth International Symposium on Robotics Research, 2013

Barfoot, Tim ; Brock, Oliver (2015)

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A novel type of compliant and underactuated robotic hand for dexterous grasping

Deimel, Raphael ; Brock, Oliver (2016)

The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it can accomplish and also on the complexity of the control methods required to achieve them. We believe that soft hands are able to provide diverse and robust grasping with low control complexity. They possess many mechanical degrees of freedom and are able to implement complex deformations. At the same...

Soft robotic hands for compliant grasping

Deimel, Raphael (2017)

The thesis considers the problem of grasping for autonomous robots, with a focus on the design and construction of robotic hands and grippers. The approach we take is to fundamentally reconsider the basic motivation and goals for grasping that steer hand design. We consider grasping as the result of reliable and robust patterns of interaction between hand, object and environment which are mecha...

On decomposability in robot reinforcement learning

Höfer, Sebastian (2017)

Reinforcement learning is a computational framework that enables machines to learn from trial-and-error interaction with the environment. In recent years, reinforcement learning has been successfully applied to a wide variety of problem domains, including robotics. However, the success of the reinforcement learning applications in robotics relies on a variety of assumptions, such as the availab...