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Found 4 papers, 1 papers with code
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks.
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite, which provides three main improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a C++ library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, simplifying their selection during the execution; 3) the new distributed simulation capability allows simulating complex scenarios while sharing the load on multiple workers and machines.
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in robotics.
Incremental Semiparametric Inverse Dynamics Learning
This paper presents a novel approach for incremental semiparametric inverse dynamics learning.
