Chiara Bartolozzi

Istituto Italiano di Tecnologia | IIT · iCub Facility

Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to the loss of informative points, slowing down prediction convergence and reducing the accuracy. In this paper, we propose to exploit...

Neuromorphic systems bring a paradigm shift in the way computation is performed in robotics. Traditional computing and deep learning supported the recent progress of robotics, but lack robustness and adaptation to bring robots operating in ever-changing scenarios and tasks. Neuromorphic intelligence develops brain-inspired supporting technology fea...

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible...

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible...

Current low latency neuromorphic processing systems, and future ones based on ultra-low power mixed-signal circuits in advanced technology nodes and memristive nano-scale devices, hold great potential for developing autonomous artificial agents. However, the variable nature and low precision of the underlying hardware substrate pose severe challeng...

Attention leads the gaze of the observer towards interesting items, allowing a detailed analysis only for selected regions of a scene. A robot can take advantage of the perceptual organisation of the features in the scene to guide its attention to better understand its environment. Current bottom–up attention models work with standard RGB cameras r...

Low latency and accuracy are fundamental requirements when vision is integrated in robots for high-speed interaction with targets, since they affect system reliability and stability. In such a scenario, the choice of the sensor and algorithms is important for the entire control loop. The technology of event-cameras can guarantee fast visual sensing...

To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for stand...

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated in a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine l...

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building co...

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer te...

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use; random motion using a live camera in an unconstrained environment....

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is...

In this chapter we describe the history and evolution of the iCub humanoid platform. We start by describing the first version as it was designed during the RobotCub EU project and illustrate how it evolved to become the platform that is adopted by more than 30 laboratories world wide. We complete the chapter by illustrating some of the research act...

Event camera (EC) emerges as a bio-inspired sensor which can be an alternative or complementary vision modality with the benefits of energy efficiency, high dynamic range, and high temporal resolution coupled with activity dependent sparse sensing. In this study we investigate with ECs the problem of face pose alignment, which is an essential pre-p...

Neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents. However, many building blocks for developing a fully neuromorphic artificial agent are still missing. While neuromorphic sensing, perception, and decision-making building blocks are quite...

Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. While neuromorphic sensing and perception, as well as decision-making systems, are now mature, the contr...

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envisio...

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional...

In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. T...

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information conten...

This paper investigates solutions to trajectory prediction problems for artificial intelligence in robotics, to improve moving target interception, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be solved with regression-based prediction, and as such, we look to learning methods. In addition, fast-moving...

In this work, we propose a new method to address audiovisual target speaker extraction in multi-talker environments using event-driven cameras. All audiovisual speech separation approaches use a frame-based video to extract visual features. However, these frame-based cameras usually work at 30 frames per second. This limitation makes it difficult t...

The Asynchronous Time-based Image Sensor (ATIS) and the Spiking Neural Network Architecture (SpiNNaker) are both neuromorphic technologies that "unconventionally" use binary spikes to represent information. The ATIS produces spikes to represent the change in light falling on the sensor, and the SpiNNaker is a massively parallel computing platform t...

Autonomous robots can rely on attention mech-anisms to explore complex scenes and select salient stimulirelevant for behaviour. Stimulus selection should be fast to effi-ciently allocate available (and limited) computational resourcesto process in detail a subset of the otherwise overwhelminglylarge sensory input. The amount of processing required...

We developed an Impulse-Based Asynchronous Serial Address-Event Representation (IB-AS-AER) protocol. It allows for full-duplex communication and explicit flow control, does not require any clock data recovery or accurate clock relationship between the transmitter and receiver. Moreover, the optical fiber communication link, that galvanically isolat...

Homeostatic plasticity is a stabilizing mechanism that allows neural systems to maintain their activity around a functional operating point. This is an extremely useful mechanism for neuromorphic computing systems, as it can be used to compensate for chronic shifts, for example due to changes in the network structure. However, it is important that...

Asynchronous, event-driven, sampling techniques adapt the sampling rate of sensory signals to their dynamics, by effectively compressing the data with respect to synchronous, clock-driven, sampling. In robotics such techniques offer data and bandwidth reduction, together with high temporal resolution and low latency. Despite vision and auditory eve...

Event cameras are bio-inspired sensors that work radically different from traditional cameras. Instead of capturing images at a fixed rate, they measure per-pixel brightness changes asynchronously. This results in a stream of events, which encode the time, location and sign of the brightness changes. Event cameras posses outstanding properties comp...

Event cameras are bio-inspired sensors that work radically different from traditional cameras. Instead of capturing images at a fixed rate, they measure per-pixel brightness changes asynchronously. This results in a stream of events, which encode the time, location and sign of the brightness changes. Event cameras posses outstanding properties comp...

Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, current trends in state-of-the-art visual algorithms rel...

An ever increasing amount of robotic platforms are being equipped with a new generation of neuromorphic computing architectures. Neuromorphic computing systems represent a promising brain-inspired technology that use asynchronous pulses to encode, transmit, and process sensory signals, typically implemented in compact low-latency and low-power devi...

Flexible organic transistors transmit force signals encoded for direct input to human neurons

In this paper, we present a device for low-latency compressive pressure sensing based on the use of a piezoelectric material coupled to mixed-mode neuromorphic CMOS circuits. The sensing element is based on the POSFET (Piezoelectric Oxide-Semiconductor-Field-Effect-Transistor) device, whereby the polarization induced by pressure in the piezoelectri...

Event-driven (ED) cameras are an emerging technology that sample the visual signal based on changes in the signal magnitude, rather than at a fixed-rate over time. The change in paradigm results in a camera with a lower latency, that uses less power, has reduced bandwidth, and higher dynamic range. Such cameras offer many potential advantages for o...

The iCub open-source humanoid robot child is a successful initiative supporting research in embodied artificial intelligence.

Homeostatic plasticity is a stabilizing mechanism commonly observed in real neural systems that allows neurons to maintain their activity around a functional operating point. This phenomenon can be used in neuromorphic systems to compensate for slowly changing conditions or chronic shifts in the system configuration. However, to avoid interference...

Event cameras offer many advantages over standard frame-based cameras, such as low latency, high temporal resolution, and a high dynamic range. They respond to pixel-level brightness changes and, therefore, provide a sparse output. However, in textured scenes with rapid motion, millions of events are generated per second. Therefore, state-of-the-ar...

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for e...

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for e...

In this paper, we present a new version of previously fabricated event driven tactile sensor [1] with modifications across its circuits and methods involved using the AMS CMOS 0.18μm technology. Electrical characterization experimental results are shown and compared to the ones from the previous proving the advantages of the new sensor concerning a...

We present a low-power compact circuit for the event-driven readout of tactile sensors. The taxel is based on the POSFET device, a sensotronic unit where a piezo-electric material is deposited on the gate of a transistor, integrated with a Leaky-Integrate and Fire neuron circuit. This device encodes the applied force with trains of digital pulses,...

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are...

Vergence control and tracking allow a robot to maintain an accurate estimate of a dynamic object three dimensions , improving depth estimation at the fixation point. Brain-inspired implementations of vergence control are based on models of complex binocular cells of the visual cortex sensitive to disparity. The energy of cells activation provides a...

This article reviews a wide spectrum of state-of-the-art neuromorphic systems, ranging from its principles, sensory elements, and processing aspects to large-scale example systems and commercial outlook. It does not aim to provide a full coverage of present knowledge, but simply provide a comprehensive summary with many pointers to further readings...

Homeostatic plasticity is a stabilizing mechanism that allows neural systems to maintain their activity around a functional operating point. This is an extremely useful mechanism for neuromorphic computing systems, as it can be used to compensate for chronic shifts, for example due to changes in the network structure. However, it is important that...

Tactile sensors provide robots with the ability to interact with humans and the environment with great accuracy, yet technical challenges remain for electronic-skin systems to reach human-level performance.