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2020


FootTile: a Rugged Foot Sensor for Force and Center of Pressure Sensing in Soft Terrain
FootTile: a Rugged Foot Sensor for Force and Center of Pressure Sensing in Soft Terrain

Felix Ruppert, , Badri-Spröwitz, A.

In Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, International Conference on Robotics and Automation, May 2020 (inproceedings) Accepted

Abstract
In this paper, we present FootTile, a foot sensor for reaction force and center of pressure sensing in challenging terrain. We compare our sensor design to standard biomechanical devices, force plates and pressure plates. We show that FootTile can accurately estimate force and pressure distribution during legged locomotion. FootTile weighs 0.9g, has a sampling rate of 330 Hz, a footprint of 10×10 mm and can easily be adapted in sensor range to the required load case. In three experiments, we validate: first, the performance of the individual sensor, second an array of FootTiles for center of pressure sensing and third the ground reaction force estimation during locomotion in granular substrate. We then go on to show the accurate sensing capabilities of the waterproof sensor in liquid mud, as a showcase for real world rough terrain use.

Youtube1 Youtube2 Presentation link (url) [BibTex]

2020

Youtube1 Youtube2 Presentation link (url) [BibTex]

2019


A Learnable Safety Measure
A Learnable Safety Measure

Heim, S., Rohr, A. V., Trimpe, S., Badri-Spröwitz, A.

Conference on Robot Learning, November 2019 (conference) Accepted

Arxiv [BibTex]

2019

Arxiv [BibTex]


Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots
Trunk Pitch Oscillations for Joint Load Redistribution in Humans and Humanoid Robots

Drama, Ö., Badri-Spröwitz, A.

Proceedings International Conference on Humanoid Robots, Humanoids, September 2019 (conference) Accepted

link (url) [BibTex]

link (url) [BibTex]


The positive side of damping
The positive side of damping

Heim, S., Millard, M., Le Mouel, C., Sproewitz, A.

Proceedings of AMAM, The 9th International Symposium on Adaptive Motion of Animals and Machines, August 2019 (conference) Accepted

[BibTex]

[BibTex]


Quantifying the Robustness of Natural Dynamics: a Viability Approach
Quantifying the Robustness of Natural Dynamics: a Viability Approach

Heim, S., Sproewitz, A.

Proceedings of Dynamic Walking , Dynamic Walking , 2019 (conference) Accepted

Submission DW2019 [BibTex]

Submission DW2019 [BibTex]

2018


Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fail with Grace
Learning from Outside the Viability Kernel: Why we Should Build Robots that can Fail with Grace

Heim, S., Sproewitz, A.

Proceedings of SIMPAR 2018, pages: 55-61, IEEE, 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), May 2018 (conference)

link (url) DOI Project Page [BibTex]

2018

link (url) DOI Project Page [BibTex]


Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware

Heim, S., Ruppert, F., Sarvestani, A., Sproewitz, A.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2018, pages: 5076-5081, IEEE, International Conference on Robotics and Automation, May 2018 (inproceedings)

Abstract
Learning instead of designing robot controllers can greatly reduce engineering effort required, while also emphasizing robustness. Despite considerable progress in simulation, applying learning directly in hardware is still challenging, in part due to the necessity to explore potentially unstable parameters. We explore the of concept shaping the reward landscape with training wheels; temporary modifications of the physical hardware that facilitate learning. We demonstrate the concept with a robot leg mounted on a boom learning to hop fast. This proof of concept embodies typical challenges such as instability and contact, while being simple enough to empirically map out and visualize the reward landscape. Based on our results we propose three criteria for designing effective training wheels for learning in robotics.

Video Youtube link (url) Project Page [BibTex]

Video Youtube link (url) Project Page [BibTex]

2017


Scalable Pneumatic and Tendon Driven Robotic Joint Inspired by Jumping Spiders
Scalable Pneumatic and Tendon Driven Robotic Joint Inspired by Jumping Spiders

Sproewitz, A., Göttler, C., Sinha, A., Caer, C., Öztekin, M. U., Petersen, K., Sitti, M.

In Proceedings 2017 IEEE International Conference on Robotics and Automation (ICRA), pages: 64-70, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), May 2017 (inproceedings)

Video link (url) DOI Project Page [BibTex]

2017

Video link (url) DOI Project Page [BibTex]


Linking {Mechanics} and {Learning}
Linking Mechanics and Learning

Heim, S., Grimminger, F., Drama, Ö., Spröwitz, A.

In Proceedings of Dynamic Walking 2017, 2017 (inproceedings)

[BibTex]

[BibTex]


Is Growing Good for Learning?
Is Growing Good for Learning?

Heim, S., Spröwitz, A.

Proceedings of the 8th International Symposium on Adaptive Motion of Animals and Machines AMAM2017, 2017 (conference)

[BibTex]

[BibTex]