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Human-to-Robot Handover Based on Reinforcement Learning.

Sensors (Basel, Switzerland)
This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots' ability to safely exchange diverse objects with humans during human-robo...

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

IEEE transactions on biomedical circuits and systems
Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditionin...

A neural network model of differentiation and integration of competing memories.

eLife
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, the...

Quo vadis, planning?

The Behavioral and brain sciences
Deep meta-learning is the driving force behind advances in contemporary AI research, and a promising theory of flexible cognition in natural intelligence. We agree with Binz et al. that many supposedly "model-based" behaviours may be better explained...

Meta-learning as a bridge between neural networks and symbolic Bayesian models.

The Behavioral and brain sciences
Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge ...

The hard problem of meta-learning is what-to-learn.

The Behavioral and brain sciences
Binz et al. highlight the potential of meta-learning to greatly enhance the flexibility of AI algorithms, as well as to approximate human behavior more accurately than traditional learning methods. We wish to emphasize a basic problem that lies under...

Surface and deep learning: a blended learning approach in preclinical years of medical school.

BMC medical education
BACKGROUND: Significant challenges are arising around how to best enable peer communities, broaden educational reach, and innovate in pedagogy. While digital education can address these challenges, digital elements alone do not guarantee effective le...

Teaching Motor Skills Without a Motor: A Semi-Passive Robot to Facilitate Learning.

IEEE transactions on haptics
Semi-passive rehabilitation robots resist and steer a patient's motion using only controllable passive force elements (e.g., controllable brakes). Contrarily, passive robots use uncontrollable passive force elements (e.g., springs), while active robo...

Learning by thinking in natural and artificial minds.

Trends in cognitive sciences
Canonical cases of learning involve novel observations external to the mind, but learning can also occur through mental processes such as explaining to oneself, mental simulation, analogical comparison, and reasoning. Recent advances in artificial in...

Elements of episodic memory: insights from artificial agents.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Many recent artificial intelligence (AI) systems take inspiration from biological episodic memory. Here, we ask how these 'episodic-inspired' AI systems might inform our understanding of biological episodic memory. We discuss work showing that these ...