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ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots
Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size.
Identifying Important Sensory Feedback for Learning Locomotion Skills
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings.
DeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills
Consistent with quadruped animal data, we show that the walk-trot gait transition for quadruped robots on flat terrain improves both viability and energy efficiency.
Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive
Moreover, our investigation shows that sensing the front feet distances to the gap is the most important and sufficient sensory information for learning gap crossing.
Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion
2) What are the effects of using a memory-enabled vs. a memory-free policy network with respect to robustness, energy-efficiency, and tracking performance in sim-to-real navigation tasks?
