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Found 3 papers, 0 papers with code
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world.
Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots
In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification and our novel residual physics learning method, Environment Mimic (EnvMimic).
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Compared with the TGs, FSMs offer high-level management on each leg motion generator and enable a flexible state arrangement, which makes the learned behavior less vulnerable to unseen perturbations or challenging terrains.
