Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
The informational dysregulation framework of addiction (IDFA): an information-processing model of relapse in opioid use disorder. [PDF]
Albert OM.
europepmc +1 more source
Asymmetry in Skipping Enhances Viability Against Control Input Noise
Quadruped animals use asymmetric galloping gaits at high speeds, yet the functional role of this asymmetry remains unclear. This study shows that left–right asymmetry in touchdown angles enhances robustness to control noise. Using a simple two‐legged locomotion model and viability theory, it demonstrates that asymmetric skipping substantially enlarges ...
Yuichi Ambe, Alvin So, Shinya Aoi
wiley +1 more source
Parent-reported children's self-efficacy in linking family resources to preschoolers' learning dispositions: a mixed-methods study. [PDF]
Wang T, Qu H, Wu P, Zhu X, Wu B.
europepmc +1 more source
Alert-Driven Active Defense for IoT-Enabled CBTC Systems Using Bayesian Hypergame Modeling and Hierarchical Reinforcement Learning. [PDF]
Zhao J +6 more
europepmc +1 more source
Implicit leadership and multidimensional imitation: a structural pathway model among Chinese university students. [PDF]
Zhang J +5 more
europepmc +1 more source
Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring. [PDF]
Zhang Z.
europepmc +1 more source
A mathematical analysis of math anxiety dynamics using a classical SAS model: Bridging epidemic theory and pedagogical implications. [PDF]
Islam S, Iqbal D, Saha M, Saha G.
europepmc +1 more source
Reactive Chemistry at the Unrestricted Coupled Cluster Level: High-Throughput Calculations for Training Machine Learning Potentials. [PDF]
Allen AEA +10 more
europepmc +1 more source
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Principled reward shaping for reinforcement learning via lyapunov stability theory
Neurocomputing, 2020Abstract Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effectively accelerate the training ...
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