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Found 11 papers, 0 papers with code
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Our experimental results indicate that SATA demonstrates remarkable compliance and safety, even in challenging environments such as soft/slippery terrain or narrow passages, and under significant external disturbances, highlighting its potential for practical deployments in human-centric and safety-critical scenarios.
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?
CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion
In this letter, we present a method for integrating central pattern generators (CPGs), i. e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to produce robust and omnidirectional quadruped locomotion.
Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning
Policies can be learned in only a few million time steps, even for challenging tasks of running over rough terrain with loads of over 100% of the nominal quadruped mass.
Robust Quadruped Jumping via Deep Reinforcement Learning
In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments, such as off of uneven terrain and with variable robot dynamics parameters.
Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning
Recent breakthroughs both in reinforcement learning and trajectory optimization have made significant advances towards real world robotic system deployment.
Training in Task Space to Speed Up and Guide Reinforcement Learning
Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems.
