Robotics and Biology Laboratory

Robotics and Biological Behavior

Robotics is an interdisciplinary field that seeks to build and execute behavior, with the ultimate goal of creating machines that can operate autonomously in dynamic and uncertain environments. As such, it can benefit from insights from other areas of study, including biology and psychology. By studying the behavior of animals, including humans, we can gain insights into how they exhibit generalization capabilities, problem-solving skills, and adaptivity, and apply these insights to improve the performance of current robots. However, this requires not only applying established research methodologies, but also developing new research methodologies that are better suited to studying the underlying principles of intelligent behavior.

Ongoing projects

Intelligent Kinematic Problem Solving

Robots need to be able to understand and manipulate kinematic structures such as windows, door or drawers. We can draw inspiration from animals such as Goffin's cockatoos to teach robots these skills. Although these cockatoos certainly did not evolve to solve kinematic puzzles, they show remarkable success in such tasks. We want to find out how this is possible and how we can equip robots with similarly robust manipulation skills.

© Felix Noak

Capabilities and consequences of recursive, hierarchical information processing in visual systems

Robotic vision benefits from insights about human visual perception. But how about the other way around? Could robot visual perception help understand human visual perception better? Using a hierarchical functional architecture for synthetic perceptual systems, we study human performance and derive principles of robust information processing in perceptual systems. With this, we simultaneously advance our understanding of human vision and incorporate the underlying principles in robot vision.

© RBO

Differentiable Interconnected Recursive Estimation as a Principle of Intelligence

We propose a computational principle for mapping sensory inputs to suitable actions, consisting of three building blocks: recursive estimators, interconnections, and differentiable programming. This integrated system can extract task-relevant information from the sensory input and generate suitable actions to achieve complex goals. We seek to study it as a model for different intelligent behaviors, thereby proposing it as a more general principle of intelligence.

Generating Robust and General Real-World Behavior by Exploiting Regularities at Multiple Levels of Abstraction

This project aims to enhance complex, robust, and general robot manipulation learning through inductive biases based on structured regularities in the perception/action space. The biases will be hierarchical and composed of regularities at different levels of abstraction. They will be validated in a contact-rich manipulation task using a highly capable hand/arm system with multi-modal sensors, resulting in a powerful and data-efficient learning approach.

© DALL·E 2

Rational Selection of Exploration Strategies in an Escape room Task

How do humans select the right strategy to solve a task? We aim to unlock the secrets of the mind's toolbox. In this project, we explore the mechanisms behind strategy selection in solving cognitive and behavioral tasks. Focusing on how the trade-off between accuracy and costs is inferred, we aim to provide a deeper understanding of the ecologically rational strategy selection process and how it can be improved.

Previous Projects

Heuristics for ball catching

We explore specialist and generalist approaches in artificial intelligence through the outfielder ball catching problem. Our analysis shows that these views lie on a spectrum, and the choice of problem representation is key. We find that, for this problem, the two views collapse to a single point on the spectrum. These findings have important implications for building smarter machines that can tackle complex decision-making problems more effectively.

Parrobots

The parrobots project was a seed funded project that we used to bootstrap our research and application for the project "Intelligent Kinematic Problem Solving". In this project we started our interdisciplinary cooperation to find out how Goffin's cockatoos can learn to solve mechanical puzzles. To this end we developed a novel experimental setup, the called the Modular Lockbox. It allows to set up new kinematic puzzles in a short time-frame, to quickly perform new experiments.

© RBO

Human Grasping

We study human grasping under a variety of conditions in order to identify and characterize different grasping strategies. Specially, we are interested in strategies that are robust to be performed under different kinds of impairment, e.g. visual. In addition to subjects' grasping with their natural hands, we also observe them when they use soft robotic hands such as RBO Hand 2 and Pisa/IIT Hand. Our final goal, is to transfer those robust strategies to a robot.

Funding

Science of Intelligence

Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2002/1 "Science of Intelligence" - project number 390523135.

Soft Manipulation (SoMa)

Soft Manipulation (SoMa) -  funded by the European Commission in the Horizon 2020 program, award number 645599, May 2015 - April 2019.

Alexander von Humboldt

Publications

2023

[en] Battaje, Aravind; Brock, Oliver; Rolfs, Martin
An interactive motion perception tool for kindergarteners (and vision scientists)
i-Perception, 14 (2) :20416695231159182
March 2023
ISSN: 2041-6695
Mengers, Vito; Battaje, Aravind; Baum, Manuel; Brock, Oliver
Combining Motion and Appearance for Robust Probabilistic Object Segmentation in Real Time
2023 IEEE International Conference on Robotics and Automation (ICRA), Page 683--689
IEEE
2023
Baum, Manuel; Froessl, Amelie; Battaje, Aravind; Brock, Oliver
Estimating the Motion of Drawers From Sound
2023 International Conference on Robotics and Automation (ICRA)
IEEE
2023
Li, Xing; Baum, Manuel; Brock, Oliver
Augmentation Enables One-Shot Generalization In Learning From Demonstration for Contact-Rich Manipulation
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2023

2022

Pannen, Tessa J.; Puhlmann, Steffen; Brock, Oliver
A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Page 10939--10944
May 2022
[english] Puhlmann, Steffen; Harris, Jason; Brock, Oliver
RBO Hand 3: A Platform for Soft Dexterous Manipulation
IEEE Transactions on Robotics :1-16
April 2022
ISSN: 1941-0468
Baum, Manuel; Brock, Oliver
"The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
2022
Battaje, Aravind; Brock, Oliver
One Object at a Time: Accurate and Robust Structure From Motion for Robots
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2022
Baum, Manuel; Schattenhofer, Lukas; R"ossler, Theresa; Osuna-Mascaró, Antonio; Auersperg, Alice; Kacelnik, Alex; Brock, Oliver
Yoking-Based Identification of Learning Behavior in Artificial and Biological Agents
Proceedings of the International Conference on the Simulation of Adaptive Behavior 2022 -From Animals to Animats 16, Page 67–78
Publisher: Springer International Publishing, Cham
2022
ISBN
978-3-031-16770-6

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