The aim of Event-Driven Perception for Robotics (EDPR) is to make robots function in full autonomy: energetically and computationally. This requires simultaneously saving on power consumption, reducing the total number of bits elaborated (or equivalently transmitted, stored, etc.) and developing efficient computation.

Autonomy brings about a number of technical requirements depending on whether we consider power consumption and computational autonomy that lead to untethered machines, or rather behavioural autonomy, whereby robots take autonomous decisions based on set goals and real-time interaction with the environment. In practice, the key to any autonomy is information encoding. Efficient encoding of sensory signals allows for an optimal representation of information, reducing the cost of acquiring, transmitting and storing unnecessary data, and simultaneously allowing for a better extraction of relevant information, which in turn enables more robust decision-making. 

Biological systems are autonomous in the sense described above, as evolution developed computational strategies for making sense of the external noisy and ambiguous signals to produce appropriate behaviour in real time, at the lowest possible energetic cost and using an inhomogeneous substrate for computation comprising slow and stochastic elements. The properties of biological systems are a reference point for the realisation of robots that face similar computational and energetic constraints and have to replicate basic human skills for reliable and robust interaction with the environment and cooperation with humans. 

Event-Driven Perception for Robotics will induce a paradigm shift in robotics, based on the biologically inspired emerging concept of event-driven sensing and processing that leads to better robots able to acquire, transmit and process information only when needed, optimising the use of resources, leading to real-time, low-cost, operation.