Robotics seminars

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

Multistable structures can reversibly change between multiple stable configurations when a sufficient energetic input is provided. While originally the field focused on understanding what governs the snapping, more recently it has been shown that these systems also provide a powerful platform to design a wide range of smart structures. In this talk, I will first show that pressure-deployable origami structures characterized by two stable configurations provide opportunities for a new generation of large-scale inflatable structures that lock in place after deployment and provide a robust enclosure through their rigid faces. Then, I will demonstrate that the propagation of transition waves in a bistable one-dimensional linkage can be exploited as a robust mechanism to realize structures that can be quickly deployed. Finally, while in the first two examples multistability is harnessed to realize deployable architectures, I will demonstrate that bistable building blocks can also be exploited to design crawling and jumping robots. Unlike previously proposed robots that require complex input control of multiple actuators, a simple, slow input signal suffices to make our system move, as all features required for locomotion are embedded into the architecture of the building blocks.

From slithering snakes, to entangling robots, self-deforming (shape changing) active systems display surprising dynamics. This is particularly true when such systems interact with environments or other agents to generate self-propulsion (movement). In this talk, I will discuss a few projects from my group illustrating unexpected effects in individual and collectives of self-deformers. For example, snakes and snake-like robots mechanically “diffract” from fixed environmental heterogeneities, collections of smart-active robots (smarticles) can locomote (and phototax) as a collective despite individual immobility, and geometrically actively entangling ensembles of blackworms and robots can self-propel as a unit to thermo or phototax without centralized control.

Deep learning is a prime example of how brain-inspired computing can benefit development of artificial intelligence. But what else can we learn from the brain for bringing AI and robotics to the next level? Energy efficiency and data efficiency are the major features of the brain and human cognition that today’s deep learning has yet to deliver. The brain can be seen as a multi-agent system of heterogeneous learners using different representations and algorithms. The flexible use of reactive, model-free control and model-based “mental simulation” appears to be the basis for computational and data efficiency of the brain. How the brain efficiently acquires and flexibly combines prediction and control modules is a major open problem in neuroscience and its solution should help developments of more flexible and autonomous AI and robotics.