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A Dynamic Movement Primitives and Reinforcement Learning Framework for Rapid Integration and Deployment on Collaborative Robots

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-62476-7_13openalex-snapshot

Allowing a Real Collaboration Between Humans and Robots

inferredcites →https://doi.org/10.1007/978-3-031-01570-0openalex-snapshot

Robot Learning from Human Teachers

inferredcites →https://doi.org/10.1007/s11370-021-00387-2openalex-snapshot

Reinforcement learning-based dynamic obstacle avoidance and integration of path planning

inferredcites →https://doi.org/10.1016/j.robot.2021.103844openalex-snapshot

Overcoming some drawbacks of Dynamic Movement Primitives

inferredcites →https://doi.org/10.1109/icar46387.2019.8981552openalex-snapshot

Dynamic Movement Primitives: Volumetric Obstacle Avoidance

inferredcites →https://doi.org/10.1109/icra.2018.8460756openalex-snapshot

Composable Deep Reinforcement Learning for Robotic Manipulation

inferredcites →https://doi.org/10.1109/irds.2002.1041514openalex-snapshot

Learning rhythmic movements by demonstration using nonlinear oscillators

inferredcites →https://doi.org/10.1109/tpami.2018.2844175openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1162/neco_a_00393openalex-snapshot

Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors

inferredcites →https://doi.org/10.3390/app112311184openalex-snapshot

Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance

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