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2018


Deep Reinforcement Learning for Event-Triggered Control
Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

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arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


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Assessment Of Atypical Motor Development In Infants Through Toy-Stimulated Play And Center Of Pressure Analysis

Zhao, S., Mohan, M., Torres, W. O., Bogen, D. K., Shofer, F. S., Prosser, L., Loeb, H., Johnson, M. J.

In Proceedings of the Annual Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) Conference, Arlington, USA, July 2018 (inproceedings)

Abstract
There is a need to identify measures and create systems to assess motor development at an early stage. Center of Pressure (CoP) is a quantifiable metric that has been used to investigate postural control in healthy young children [6], children with CP [7], and infants just beginning to sit [8]. It was found that infants born prematurely exhibit different patterns of CoP movement than infants born full-term when assessing development impairments relating to postural control [9]. Preterm infants exhibited greater CoP excursions but had greater variability in their movements than fullterm infants. Our solution, the Play And Neuro-Development Assessment (PANDA) Gym, is a sensorized environment that aims to provide early diagnosis of neuromotor disorder in infants and improve current screening processes by providing quantitative measures rather than subjective ones, and promoting natural play with the stimulus of toys. Previous studies have documented stages in motor development in infants [10, 11], and developmental delays could become more apparent through toy interactions. This study examines the sensitivity of the pressure-sensitive mat subsystem to detect differences in CoP movement patterns for preterm and fullterm infants less than 6 months of age, with varying risk levels. This study aims to distinguish between typical and atypical motor development through assessment of the CoP data of infants in a natural play environment, in conditions where movement may be further stimulated with the presence of a toy.

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link (url) [BibTex]

link (url) [BibTex]


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)

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link (url) DOI Project Page [BibTex]

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.

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Video Youtube link (url) Project Page [BibTex]

Video Youtube link (url) Project Page [BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


Systematic self-exploration of behaviors for robots in a dynamical systems framework
Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


Learning equations for extrapolation and control
Learning equations for extrapolation and control

Sahoo, S. S., Lampert, C. H., Martius, G.

In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

Abstract
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

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Code Arxiv Poster Slides link (url) Project Page [BibTex]

Code Arxiv Poster Slides link (url) Project Page [BibTex]


Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Sun, H., Martius, G.

Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

Abstract
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]

2015


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Human Machine Interface for Dexto Eka: - The humanoid robot

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the IEEE International Conference on Robotics, Automation, Control and Embedded Systems (RACE), Chennai, India, Febuary 2015 (inproceedings)

Abstract
This paper illustrates hybrid control system of the humanoid robot, Dexto:Eka: focusing on the dependent or slave mode. Efficiency of any system depends on the fluid operation of its control system. Here, we elucidate the control of 12 DoF robotic arms and an omnidirectional mecanum wheel drive using an exo-frame, and a Graphical User Interface (GUI) and a control column. This paper comprises of algorithms, control mechanisms and overall flow of execution for the regulation of robotic arms, graphical user interface and locomotion.

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DOI [BibTex]

2015


DOI [BibTex]


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Conception and development of Dexto:Eka: The Humanoid Robot - Part IV

Kumra, S., Mohan, M., Vaswani, H., Gupta, S.

In Proceedings of the IEEE International Conference on Robotics, Automation, Control and Embedded Systems (RACE), Febuary 2015 (inproceedings)

Abstract
This paper elucidates the fourth phase of the development of `Dexto:Eka: - The Humanoid Robot'. It lays special emphasis on the conception of the locomotion drive and the development of vision based system that aids navigation and tele-operation. The first three phases terminated with the completion of two robotic arms with six degrees of freedom each, structural development and the creation of a human machine interface that included an exo-frame, a control column and a graphical user interface. This phase also involved the enhancement of the exo-frame to a vision based system using a Kinect camera. The paper also focuses on the reasons behind choosing the locomotion drive and the benefits it has.

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DOI [BibTex]

DOI [BibTex]

2013


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Governance of Humanoid Robot Using Master Exoskeleton

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the IEEE International Symposium on Robotics (ISR), Seoul, South Korea, October 2013 (inproceedings)

Abstract
Dexto:Eka: is an adult-size humanoid robot being developed with the aim of achieving tele-presence. The paper sheds light on the control of this robot using a Master Exoskeleton which comprises of an Exo-Frame, a Control Column and a Graphical User Interface. It further illuminates the processes and algorithms that have been utilized to make an efficient system that would effectively emulate a tele-operator.

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DOI [BibTex]

2013


DOI [BibTex]


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Design and development part 2 of Dexto:Eka: - The humanoid robot

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, August 2013 (inproceedings)

Abstract
Through this paper, we elucidate the second phase of the design and development of the tele-operated humanoid robot Dexto:Eka:. Phase one comprised of the development of a 6 DoF left anthropomorphic arm and left exo-frame. Here, we illustrate the development of the right arm, right exo-frame, torso, backbone, human machine interface and omni-directional locomotion system. Dexto:Eka: will be able to communicate with a remote user through Wi-Fi. An exo-frame capacitates it to emulate human arms and its locomotion is controlled by joystick. A Graphical User Interface monitors and helps in controlling the system.

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DOI [BibTex]

DOI [BibTex]

2008


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

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DOI [BibTex]

2008


DOI [BibTex]


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Structure from Behavior in Autonomous Agents

Martius, G., Fiedler, K., Herrmann, J.

In Proc. IEEE Intl. Conf. Intelligent Robots and Systems (IROS 2008), pages: 858 - 862, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]

2006


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Let It Roll – Emerging Sensorimotor Coordination in a Spherical Robot

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

In Proc, Artificial Life X, pages: 192-198, Intl. Society for Artificial Life, MIT Press, August 2006 (inproceedings)

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[BibTex]

2006


[BibTex]


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From Motor Babbling to Purposive Actions: Emerging Self-exploration in a Dynamical Systems Approach to Early Robot Development

Der, R., Martius, G.

In Proc. From Animals to Animats 9, SAB 2006, 4095, pages: 406-421, LNCS, Springer, 2006 (inproceedings)

Abstract
Self-organization and the phenomenon of emergence play an essential role in living systems and form a challenge to artificial life systems. This is not only because systems become more lifelike, but also since self-organization may help in reducing the design efforts in creating complex behavior systems. The present paper studies self-exploration based on a general approach to the self-organization of behavior, which has been developed and tested in various examples in recent years. This is a step towards autonomous early robot development. We consider agents under the close sensorimotor coupling paradigm with a certain cognitive ability realized by an internal forward model. Starting from tabula rasa initial conditions we overcome the bootstrapping problem and show emerging self-exploration. Apart from that, we analyze the effect of limited actions, which lead to deprivation of the world model. We show that our paradigm explicitly avoids this by producing purposive actions in a natural way. Examples are given using a simulated simple wheeled robot and a spherical robot driven by shifting internal masses.

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[BibTex]

[BibTex]

2005


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Learning to Feel the Physics of a Body

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

In Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 , 2, pages: 252-257, Washington, DC, USA, 2005 (inproceedings)

Abstract
Despite the tremendous progress in robotic hardware and in both sensorial and computing efficiencies the performance of contemporary autonomous robots is still far below that of simple animals. This has triggered an intensive search for alternative approaches to the control of robots. The present paper exemplifies a general approach to the self-organization of behavior which has been developed and tested in various examples in recent years. We apply this approach to an underactuated snake like artifact with a complex physical behavior which is not known to the controller. Due to the weak forces available, the controller so to say has to develop a kind of feeling for the body which is seen to emerge from our approach in a natural way with meandering and rotational collective modes being observed in computer simulation experiments.

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[BibTex]

2005


[BibTex]