Stanford prison experiment

The Stanford prison experiment (SPE) was a psychological experiment conducted in the summer of 1971. It was a two-week simulation of a prison environment that examined the effects of situational variables on participants' reactions and behaviors. Stanford University psychology professor Philip Zimbardo led the research team who administered the study. Participants were recruited from the local community with an ad in the newspapers offering $15 per day to male students who wanted to participate in a "psychological study of prison life." Volunteers were chosen after assessments of psychological stability and then randomly assigned to being prisoners or prison guards. Critics have questioned the validity of these methods. Those volunteers selected to be "guards" were given uniforms specifically to de-individuate them, and they were instructed to prevent prisoners from escaping. The experiment officially started when "prisoners" were arrested by real Palo Alto police. Over the followi

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A computational modeling approach to study the neuromechanics of terrestrial locomotion

Shravan Tata Ramalingasetty

The main theme of my thesis will be to use neuro-muscular modeling techniques to study locomotion of terrestrial mammals. Locomotion is the ability of animals to interact with the environment to propel themselves in space. It involves co-ordination and precise timing of tens of muscles and multiple joints to produce rhythmic, efficient and robust gaits. It is now understood that it is a complex interaction of brain, spinal cord, the musculo-skeletal system and the environment. During my thesis I plan to develop models of rodents to non-human primates to humans to study the interactions between these components. I will be carrying out my research at BioRob lab and funded by the Human Brain Project. After spending an year on this subject currently I have developed a neuro-muscular hind limb model of a mouse. I am using this model to study different locomotion theories ranging from reflex based abstraction methods to Central Patter Generator networks and spiking neuron based spinal circuits. The closed loop approach seems promising and I am now able to generate stable locomotion on a forward dynamics model using all the mentioned frameworks. The future plan will be to further improve the rodent model with more degrees of freedom in the forelimb and extend the same modeling approach to develop non-human primate and human models to carry out similar experiments.

EPFL2022

Neuromuscular model achieving speed control and steering with a 3D bipedal walker

Auke Ijspeert, Renaud Ronsse, Nicolas Benoît Dominique Van der Noot

Nowadays, very few humanoid robots manage to travel in our daily environments. This is mainly due to their limited locomotion capabilities, far from the human ones. Recently, we developed a bio-inspired torque-based controller recruiting virtual muscles driven by reflexes and a central pattern generator. Straight walking experiments were obtained in a 3D simulation environment, resulting in the emergence of human-like and robust gait patterns, with speed modulation capabilities. In this paper, we extend this model, in order to control the steering direction and curvature. Based on human turning strategies, new control pathways are introduced and optimized to reach the sharpest possible turns. In sum, tele-operated motions can be achieved through the control of two scalar inputs (i.e. forward speed and heading). This is particularly relevant for steering the robot on-line, and navigating in cluttered environments. Finally, the biped demonstrated significant robustness during blind walking experiments.

SPRINGER2019

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

Auke Ijspeert, Renaud Ronsse, Jesse van den Kieboom

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.

Springer-Verlag2011

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