Robotics and Biology Laboratory

Adrian Sieler

Office MAR 5-1
Room MAR 5.065
Office HoursOnly by appointment

Research Interests

My research focuses on the consequences of the embodiment of a robotic agent to generate robust behavior when interacting with the real world. Particularly dexterous soft robot hands have properties that allow for a new perspective on one of the grand challenges in robotics - autonomous in-hand manipulation. Therefore, I am investigating how to most efficiently use the morphological computation (MC) that the hand performs when physically interacting with the environment to develop new control and planning approaches. Classical analytic and learning-based approaches are not directly applicable because they were developed with a rigid hand paradigm for manipulation in mind. This motivates the investiagtion of a new set of tools to take full advantage of the body's computational power.

Short CV

  • 06.2019 - present
  • 04.2016 - 03.2019
    • MSc Mathematics in Data Science - TU Munich
      • 04.2018 - 12.2018
        Master Thesis, Siemens AG - Corporate Technology: Mechatronic Systems
        Topic: Simulation-Based Reinforcement Learning of Complex Reflexes for Low-Level Robotic Systems
  • 08.2018 - 05.2019
  • 10.2012-04.2016
    • BSc Mathematics - TU Munich
      • 12.2015 - 03.2016  
        Bachelor Thesis
        Topic: Refinement and Coarsening of Online-Offline Data Mining Methods with Sparse Grids

Project

© Felix Noak

Dexterous and Sensorized Soft Robotic Hands

Inspired by human grasping and manipulation capabilities, we build anthropomorphic soft robotic hands with a high degree of dexterity to enable robust interactions with the environment. We develop new sensor technologies that work with the highly compliant hands, while still providing useful sensor feedback. At the same time, we further increase the robustness of soft hands by devising control methods that reduce perceptual, model, and motion uncertainty through haptic feedback.

Supervised Theses

© Posifa

Robustifying Air-Mass Control of Soft-Pneumatic-Actuators with Air-Flow Sensors

Joel Simon Fuchs, November 2021

Air mass control for soft pneumatic actuators is the proper actuation scheme to avoid compromising the intrinsic compliance of the system during control. The enclosed air-mass in a soft system is independent of shape changes during interaction with the environment. In this work, we investigate different airflow sensors to increase the accuracy of our current data-driven approach to air mass control.

Analysis of Soft Finger Pulp Design on Grasping and Manipulation

Vipul Mahawar, August 2021

The goal of this master's thesis is to analyse how different materials and morphologies can change the grasping and manipulation behavior of soft robot hands. In this thesis you will build robot fingers from different kinds of soft materials and will experiment with different hand morphologies. During the course of this thesis we hope to understand how we can build robot hands that are not only more dexterous, but also more robust in their behavior.

Vision-Based Teleoperation of the Compliant RBO Hand 3

Friederike Thonagel, May 2022

Using an off-the-shelf computer vision tool, a multi-camera setup tracks the user's hand and wrist postures to estimate 3D hand pose. Human joint angles are mapped to the RBO Hand 3 in simulation and on a robotic hand. The setup's quality is evaluated by performing complex in-hand manipulations. This work bridges the gap between theory and practice in contact dynamics, providing insights for more realistic modeling.

© Sumit Patidar

In-Hand Manipulation via Constraint Exploitation and Wrist-Movements

Sumit Patidar, September 2022

The advantages of wrist movements for hand manipulation have received little attention in robotics. Most approaches use only the capabilities of the fingers of a hand. Humans constantly move their wrist to take advantage of gravity or inertial forces to support the desired manipulation. In this work, we explore the role of external resources (gravity, inertia) in the context of exploiting constraints for hand manipulation.

© RBO

Learning of Drift-Prediction Models to Increase the Accuracy of Air-Mass Controllers

Mohamed Rekik Farouk

Air mass control for soft pneumatic actuators is the proper actuation scheme to avoid compromising the intrinsic compliance of the system during control. The enclosed air-mass in a soft system is independent of shape changes during interaction with the environment. In this work, we investigate different data-driven techniques to increase the accuracy of a given air mass controller.

Publications

2023

Sieler, Adrian; Brock, Oliver
Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation
Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)
2023
Patidar, Sumit; Sieler, Adrian; Brock, Oliver
In-Hand Cube Reconfiguration: Simplified
Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)
2023

2021

Bhatt, Aditya; Sieler, Adrian; Puhlmann, Steffen; Brock, Oliver
Surprisingly Robust In-Hand Manipulation: An Empirical Study
Proceedings of Robotics: Science and Systems
Publisher: Virtual
July 2021