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Found 7 papers, 2 papers with code
Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking
Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning.
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains.
Strong attentional modulation of V1/V2 activity implements a robust, contrast-invariant control mechanism for selective information processing
When selective attention is devoted to one of multiple stimuli within receptive fields of neurons in visual area V4, cells respond as if only the attended stimulus was present.
Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks
In the literature, there is no consensus about which feedback frequency is optimal or at which time the feedback is most beneficial.
Feature Disentanglement of Robot Trajectories
Modeling trajectories generated by robot joints is complex and required for high level activities like trajectory generation, clustering, and classification.
A Development Cycle for Automated Self-Exploration of Robot Behaviors
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors.
Combinatorics of a Discrete Trajectory Space for Robot Motion Planning
Motion planning is a difficult problem in robot control.
