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2016


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On designing an active tail for legged robots: simplifying control via decoupling of control objectives

Heim, S. W., Ajallooeian, M., Eckert, P., Vespignani, M., Ijspeert, A. J.

Industrial Robot: An International Journal, 43, pages: 338-346, Emerald Group Publishing Limited, 2016 (article)

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Preprint [BibTex]

2016


Preprint [BibTex]


Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

Mescheder, L., Nowozin, S., Geiger, A.

Arxiv, 2016 (article)

Abstract
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy to implement. Moreover, we show how our method can be combined with blocking to improve mixing. Even though our method leads to inferior mixing times compared to a sequential Gibbs sampler, we argue that our method is still very useful for large dynamic networks, where factors are added and removed on a continuous basis, as it is hard to maintain a graph coloring in this setup. Similarly, our method is useful for parallelizing Gibbs sampling in graphical models that do not allow for graph colorings with a small number of colors such as densely connected graphs.

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pdf [BibTex]


ATRIAS: Design and validation of a tether-free 3D-capable spring-mass bipedal robot
ATRIAS: Design and validation of a tether-free 3D-capable spring-mass bipedal robot

Hubicki, C., Grimes, J., Jones, M., Renjewski, D., Spröwitz, A., Abate, A., Hurst, J.

{The International Journal of Robotics Research}, 35(12):1497-1521, Sage Publications, Inc., Cambridge, MA, 2016 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Map-Based Probabilistic Visual Self-Localization
Map-Based Probabilistic Visual Self-Localization

Brubaker, M. A., Geiger, A., Urtasun, R.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


Roombots: Reconfigurable Robots for Adaptive Furniture
Roombots: Reconfigurable Robots for Adaptive Furniture

Spröwitz, A., Pouya, S., Bonardi, S., van den Kieboom, J., Möckel, R., Billard, A., Dillenbourg, P., Ijspeert, A.

Computational Intelligence Magazine, IEEE, 5(3):20-32, 2010 (article)

Abstract
Imagine a world in which our furniture moves around like legged robots, interacts with us, and changes shape and function during the day according to our needs. This is the long term vision we have in the Roombots project. To work towards this dream, we are developing modular robotic modules that have rotational degrees of freedom for locomotion as well as active connection mechanisms for runtime reconfiguration. A piece of furniture, e.g. a stool, will thus be composed of several modules that activate their rotational joints together to implement locomotor gaits, and will be able to change shape, e.g. transforming into a chair, by sequences of attachments and detachments of modules. In this article, we firstly present the project and the hardware we are currently developing. We explore how reconfiguration from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using metamodules-two Roombots modules connected serially-that use broadcast signals and connections to a structured ground to collectively build desired structures without the need of a centralized planner. We then present how locomotion controllers can be implemented in a distributed system of coupled oscillators-one per degree of freedom-similarly to the concept of central pattern generators (CPGs) found in the spinal cord of vertebrate animals. The CPGs are based on coupled phase oscillators to ensure synchronized behavior and have different output filters to allow switching between oscillations and rotations. A stochastic optimization algorithm is used to explore optimal CPG configurations for different simulated Roombots structures.

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DOI [BibTex]

DOI [BibTex]

2008


Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization
Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization

Spröwitz, A., Moeckel, R., Maye, J., Ijspeert, A. J.

The International Journal of Robotics Research, 27(3-4):423-443, 2008 (article)

Abstract
This article addresses the problem of how modular robotics systems, i.e. systems composed of multiple modules that can be configured into different robotic structures, can learn to locomote. In particular, we tackle the problems of online learning, that is, learning while moving, and the problem of dealing with unknown arbitrary robotic structures. We propose a framework for learning locomotion controllers based on two components: a central pattern generator (CPG) and a gradient-free optimization algorithm referred to as Powell's method. The CPG is implemented as a system of coupled nonlinear oscillators in our YaMoR modular robotic system, with one oscillator per module. The nonlinear oscillators are coupled together across modules using Bluetooth communication to obtain specific gaits, i.e. synchronized patterns of oscillations among modules. Online learning involves running the Powell optimization algorithm in parallel with the CPG model, with the speed of locomotion being the criterion to be optimized. Interesting aspects of the optimization include the fact that it is carried out online, the robots do not require stopping or resetting and it is fast. We present results showing the interesting properties of this framework for a modular robotic system. In particular, our CPG model can readily be implemented in a distributed system, it is computationally cheap, it exhibits limit cycle behavior (temporary perturbations are rapidly forgotten), it produces smooth trajectories even when control parameters are abruptly changed and it is robust against imperfect communication among modules. We also present results of learning to move with three different robot structures. Interesting locomotion modes are obtained after running the optimization for less than 60 minutes.

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link (url) DOI [BibTex]

2008


link (url) DOI [BibTex]


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Active tails enhance arboreal acrobatics in geckos

Jusufi, A., Goldman, D., Revzen, S., Full, R.

PNAS, 105(11):4215-4219, 2008 (article)

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link (url) [BibTex]

link (url) [BibTex]