Sensorimotor learning in a Bayesian computational model of speech communication

International audienceAlthough sensorimotor exploration is a basic process within child development, clear views on the underlying computational processes remain challenging. We propose to compare eight algorithms for sensorimotor exploration, based on three components: " accommodation " performing a compromise between goal babbling and social guidance by a master, " local extrapolation " simulating local exploration of the sensorimotor space to achieve motor generalizations and " idiosyncratic babbling " which favors already explored motor commands when they are efficient. We will show that a mix of these three components offers a good compromise enabling efficient learning while reducing exploration as much as possible

Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

Curiosity-driven phonetic learning

International audienceThis article studies how developmental phonetic learning can be guided by pure curiosity-driven exploration, also called intrinsically motivated exploration. Phonetic learning refers here to learning how to control a vocal tract to reach acoustic goals. We compare three different exploration strategies for learning the auditory-motor inverse model: random motor exploration, random goal selection with reaching, and curiosity-driven active goal selection with reaching. Using a realistic vocal tract model, we show how intrinsically motivated learning driven by competence progress can generate automatically developmental structure in both articulatory and auditory modalities, displaying patterns in line with some experimental data from infants

Diversity-driven selection of exploration strategies in multi-armed bandits

International audienceWe consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the reward signal is the diversity of effects that each strategy produces. We test the method empirically on a simulated planar robotic arm, and establish that the method is both able discriminate between strategies of dissimilar quality, even when the differences are tenuous, and that the resulting performance is competitive with the best fixed mixture of strategies

A Multi-Level Control Architecture for the Bionic Handling Assistant

Rolf M, Neumann K, Queißer J, Reinhart F, Nordmann A, Steil JJ. A Multi-Level Control Architecture for the Bionic Handling Assistant. Advanced Robotics. 2015;29(13: SI):847-859.The Bionic Handling Assistant is one of the largest soft continuum robots and very special in be- ing a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other softs robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight inte- gration of standard analytic tools, simulation, control, and state of the art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for exible experimentation towards compliant user-interaction, grasping, and online learning of internal models

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure