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Found 8 papers, 1 papers with code
Purpose for Open-Ended Learning Robots: A Computational Taxonomy, Definition, and Operationalisation
Overall, the approach enables OEL robots to learn in an autonomous way but also to focus on acquiring goals and skills that meet the purposes of the designers and users.
A method for the ethical analysis of brain-inspired AI
The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI.
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
We propose that in the cortex the same reward-based trial-and-error processes might support not only the acquisition of motor representations but also of sensory representations.
REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems
The first contribution of this work is to study the challenges posed by the previously proposed benchmark `REAL competition' aiming to foster the development of truly open-ended learning robot architectures.
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner.
Hierarchical Reinforcement Learning
Vocal Bursts Intensity Prediction
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments.
Autonomous Open-Ended Learning of Interdependent Tasks
Autonomy is fundamental for artificial agents acting in complex real-world scenarios.
Autonomous discovery of the goal space to learn a parameterized skill
The agent may posit as a task space its whole sensory space (i. e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space.
