SELFCEPTION · Robotic self/other distinction for interaction under uncertainty

Robotic self/other distinction for interaction under uncertainty

There are around 35 million private and nonindustrial robots in the world in 2019, a market of 18.7 billion euros. More specifically, the collaborative robotics and home care robotics sectors are expected to increase roughly tenfold and four fold respectively by 2020. However, autonomous robot technology in Europe is not yet ready to lead this high expectancy due to the lack of robust functionality in uncertain environments. While robotics is progressively revolutionizing industrial sectors, applications involving non-constrained or open-ended scenarios do not have robust solutions and several user-end initiatives and SMEs have disappeared. Hence, a key challenge for robotics and artificial intelligence research is developing systems that are able to autonomously interact with humans and their surrounding environment in situations that involve varying degrees of uncertainty. In fact, while humans can continuously learn from their experiences and perceive their body as a whole as they interact with the world, robots do not yet have these capabilities. Providing humanoid robots, and artificial agents in general, the capacity to perceive their body as humans do, is a breakthrough technology that goes beyond one discipline and has even philosophical and societal implications. However, this is a challenging problem that needs to revisit stablished state-of-the-art action and perception algorithms. Therefore, SELFCEPTION defined a roadmap to include some characteristics of human perception and action into artificial agents. The project developed a computational model for self/other distinction in robots inspired in current neuroscientific and psychology findings, i.e., a synthetic probabilistic model of the sensorimotor relationships that captures what the robot perceives (sensory response) and the actions that it exerts, in order to enable the machine to differentiate its own body from other elements in the environment. In essence, the robot had to answer a simple question: “is this my body?”. The unique interdisciplinary and inter-sectorial vision of this project, connecting cognitive psychology, neuroscience, artificial intelligence and robotics had two main scientific implications: i) it has reinforced the materialisation of the next generation of perceptive robots able to build its perceptual schema and distinguish its actions from other entities; and ii) it has provided some insights about how humans unconsciously maintain their own perceptual representation. Several achievements have been accomplished: • 1st implementation of Active Inference construct for body perception and action on a real humanoid robot. • 1st replication of a body-illusion into an artificial agent. • Mirror non-appearance test was passed by a humanoid robot. The project drew the following conclusions: • AI and robotics: o Self/other distinction can be achieved without being conscious. o Flexible perception and action. A flexible process for approximating the body and the world. o Combining artificial neural networks with algorithmic knowledge allows large scale cross/inter-modal sensory information decoding and adaptation. • Humans: o Under the predictive coding theory body models are affected by instantaneous bottom-up sensory cues biasing inference problems such as body localization. o We identified that action-reflexes could play a role on sensorimotor conflicts.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз