Intelligent Systems
Note: This research group has relocated.

8 results (View BibTeX file of all listed publications)

2024

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Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips

Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J.

Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (misc) Accepted

Abstract
Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.

Project Page [BibTex]

2024

Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J. Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (misc) Accepted

Project Page [BibTex]

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Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models
2024 (misc)

Abstract
Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multi-objective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning~(RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Videos: this https://sites.google.com/view/naturalwalkingrl

link (url) [BibTex]

2022

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A Sequential Group VAE for Robot Learning of Haptic Representations

Richardson, B. A., Kuchenbecker, K. J., Martius, G.

pages: 1-11, Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans, Auckland, New Zealand, December 2022 (misc)

Abstract
Haptic representation learning is a difficult task in robotics because information can be gathered only by actively exploring the environment over time, and because different actions elicit different object properties. We propose a Sequential Group VAE that leverages object persistence to learn and update latent general representations of multimodal haptic data. As a robot performs sequences of exploratory procedures on an object, the model accumulates data and learns to distinguish between general object properties, such as size and mass, and trial-to-trial variations, such as initial object position. We demonstrate that after very few observations, the general latent representations are sufficiently refined to accurately encode many haptic object properties.

link (url) Project Page [BibTex]

2022

Richardson, B. A., Kuchenbecker, K. J., Martius, G. A Sequential Group VAE for Robot Learning of Haptic Representations pages: 1-11, Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans, Auckland, New Zealand, December 2022 (misc)

link (url) Project Page [BibTex]

A Soft Vision-Based Tactile Sensor for Robotic Fingertip Manipulation
A Soft Vision-Based Tactile Sensor for Robotic Fingertip Manipulation

Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G.

Workshop paper (1 page) presented at the IROS Workshop on Large-Scale Robotic Skin: Perception, Interaction and Control, Kyoto, Japan, October 2022 (misc)

Abstract
For robots to become fully dexterous, their hardware needs to provide rich sensory feedback. High-resolution haptic sensing similar to the human fingertip can enable robots to execute delicate manipulation tasks like picking up small objects, inserting a key into a lock, or handing a cup of coffee to a human. Many tactile sensors have emerged in recent years; one especially promising direction is vision-based tactile sensors due to their low cost, low wiring complexity and high-resolution sensing capabilities. In this work, we build on previous findings to create a soft fingertip-sized tactile sensor. It can sense normal and shear contact forces all around its 3D surface with an average prediction error of 0.05 N, and it localizes contact on its shell with an average prediction error of 0.5 mm. The software of this sensor uses a data-efficient machine-learning pipeline to run in real time on hardware with low computational power like a Raspberry Pi. It provides a maximum data frame rate of 60 Hz via USB.

link (url) Project Page [BibTex]

Andrussow, I., Sun, H., Kuchenbecker, K. J., Martius, G. A Soft Vision-Based Tactile Sensor for Robotic Fingertip Manipulation Workshop paper (1 page) presented at the IROS Workshop on Large-Scale Robotic Skin: Perception, Interaction and Control, Kyoto, Japan, October 2022 (misc)

link (url) Project Page [BibTex]

2010

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\textscLpzRobots: A free and powerful robot simulator

Martius, G., Hesse, F., Güttler, F., Der, R.

\urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

[BibTex]

2010

Martius, G., Hesse, F., Güttler, F., Der, R. \textscLpzRobots: A free and powerful robot simulator \urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

[BibTex]

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Playful Machines: Tutorial

Der, R., Martius, G.

\urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

[BibTex]

Der, R., Martius, G. Playful Machines: Tutorial \urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

[BibTex]