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Showing 1 to 4 of 4 for “"Domain Randomization"”.

  1. Deep Reinforcement Learning for Multirotor Flight Control: A Comparative Study of Sim-to-Real Training and Real-World Performance

    … ablation studies evaluated the effects of domain randomization, reward weighting, observation representation, and neural network architecture. Policies were trained in a MuJoCo-based simulator and deployed via TensorFlow Lite Micro inference on PX4 flight controllers. Domain randomization

    vt Repository record for Deep Reinforcement Learning for Multirotor Flight Control: A Comparative Study of Sim-to-Real Training and Real-World Performance (opens in a new tab)

  2. Deploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apollo

    … to bridging the sim-to-real gap, leveraging domain randomization and careful choices in control architecture in order to successfully deploy RL policies for teleoperation in simulation and on hardware.

    vt Repository record for Deploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apollo (opens in a new tab)

  3. Learning Legged Locomotion by Physics-based Initialization: Motion Imitation from Model-Based Optimal Control

    … it is trained with simulated sensor noise and domain randomization, MIMOC is less sensitive to modeling and state estimation inaccuracies. We validate MIMOC on the Mini-Cheetah in outdoor environments over a wide variety of challenging terrain and on the MIT Humanoid in simulation. We show that …

    mit Repository record for Learning Legged Locomotion by Physics-based Initialization: Motion Imitation from Model-Based Optimal Control (opens in a new tab)