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2013


Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot
Benefits of an active spine supported bounding locomotion with a small compliant quadruped robot

Khoramshahi, M., Spröwitz, A., Tuleu, A., Ahmadabadi, M. N., Ijspeert, A. J.

In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages: 3329-3334, May 2013 (inproceedings)

Abstract
We studied the effect of the control of an active spine versus a fixed spine, on a quadruped robot running in bound gait. Active spine supported actuation led to faster locomotion, with less foot sliding on the ground, and a higher stability to go straight forward. However, we did no observe an improvement of cost of transport of the spine-actuated, faster robot system compared to the rigid spine.

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Youtube DOI Project Page [BibTex]

2013


Youtube DOI Project Page [BibTex]


Central pattern generators augmented with virtual model control for quadruped rough terrain locomotion
Central pattern generators augmented with virtual model control for quadruped rough terrain locomotion

Ajallooeian, M., Pouya, S., Spröwitz, A., Ijspeert, A. J.

In Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), pages: 3321-3328, IEEE, Karlsruhe, 2013 (inproceedings)

Abstract
We present a modular controller for quadruped locomotion over unperceived rough terrain. Our approach is based on a computational Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators. Stumbling correction reflex is implemented as a sensory feedback mechanism affecting the CPG. We augment the outputs of the CPG with virtual model control torques responsible for posture control. The control strategy is validated on a 3D forward dynamics simulated quadruped robot platform of about the size and weight of a cat. To demonstrate the capabilities of the proposed approach, we perform locomotion over unperceived uneven terrain and slopes, as well as situations facing external pushes.

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DOI [BibTex]

DOI [BibTex]


Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality
Gait Optimization for Roombots Modular Robots - Matching Simulation and Reality

Möckel, R., Yura, N. P., The Nguyen, A., Vespignani, M., Bonardi, S., Pouya, S., Spröwitz, A., van den Kieboom, J., Wilhelm, F., Ijspeert, A. J.

In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3265-3272, IEEE, Tokyo, 2013 (inproceedings)

Abstract
The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time consuming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robot’s behavior with the help of models of the robot and its environment. In this paper, we present a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization, both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. The presented example shows that even for robots with a low number of degrees of freedom the time required for optimization can be reduced by a factor of 2.5 to 30, at least, depending on how extensive the search for optimized control parameters should be. Time for hardware experiments becomes minimal. More importantly, gaits that can possibly damage the robotic hardware can be filtered before being tried in hardware. Yet in contrast to pure offline optimization, we reach well matched behavior that allows a direct transfer of locomotion gaits from simulation to hardware. This is because through a meta-optimization we adapt not only the locomotion parameters but also the parameters for simulation models of the robot and environment allowing for a good matching of the robot behavior in simulation and hardware. We validate the proposed hybrid optimization method on a structure composed of two Roombots modules with a total number of six degrees of freedom. Roombots are self-reconfigurable modular robots that can form arbitrary structures with many degrees of freedom through an integrated active connection mechanism.

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DOI [BibTex]

DOI [BibTex]


Modular Control of Limit Cycle Locomotion over Unperceived Rough Terrain
Modular Control of Limit Cycle Locomotion over Unperceived Rough Terrain

Ajallooeian, M., Gay, S., Tuleu, A., Spröwitz, A., Ijspeert, A. J.

In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pages: 3390-3397, Tokyo, 2013 (inproceedings)

Abstract
We present a general approach to design modular controllers for limit cycle locomotion over unperceived rough terrain. The control strategy uses a Central Pattern Generator (CPG) model implemented as coupled nonlinear oscillators as basis. Stumbling correction and leg extension reflexes are implemented as feedbacks for fast corrections, and model-based posture control mechanisms define feedbacks for continuous corrections. The control strategy is validated on a detailed physics-based simulated model of a compliant quadruped robot, the Oncilla robot. We demonstrate dynamic locomotion with a speed of more than 1.5 BodyLength/s over unperceived uneven terrains, steps, and slopes.

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DOI [BibTex]

DOI [BibTex]


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Controllability and Resource-Rational Planning

Lieder, F., Goodman, N. D., Huys, Q. J.

In Computational and Systems Neuroscience (Cosyne), pages: 112, 2013 (inproceedings)

Abstract
Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.

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[BibTex]

[BibTex]


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Learned helplessness and generalization

Lieder, F., Goodman, N. D., Huys, Q. J. M.

In 35th Annual Conference of the Cognitive Science Society, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Reverse-Engineering Resource-Efficient Algorithms

Lieder, F., Goodman, N. D., Griffiths, T. L.

In NIPS Workshop Resource-Efficient Machine Learning, 2013 (inproceedings)

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[BibTex]

[BibTex]