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Legged Robots

6 papers with code · Robots

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Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics

20 Sep 2017resibots/blackdrops

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties.

CONTINUOUS CONTROL LEGGED ROBOTS

Learning agile and dynamic motor skills for legged robots

24 Jan 2019junja94/anymal_science_robotics_supplementary

In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes.

LEGGED ROBOTS

Reset-free Trial-and-Error Learning for Robot Damage Recovery

13 Oct 2016resibots/chatzilygeroudis_2018_rte

However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously.

LEGGED ROBOTS

SGD for robot motion? The effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks

9 Oct 2017martimbrandao/legopt-benchmark

In this paper we introduce a new benchmark for trajectory optimization and posture generation of legged robots, using a pre-defined scenario, robot and constraints, as well as evaluation criteria.

LEGGED ROBOTS STOCHASTIC OPTIMIZATION

Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

20 Sep 2017resibots/pautrat_2018_mlei

One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e. g., from simulation or from previous tasks) to accelerate learning on a robot.

LEGGED ROBOTS TRANSFER LEARNING

Meta Learning Shared Hierarchies

ICLR 2018 dsapandora/s_cera

We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives---policies that are executed for large numbers of timesteps.

LEGGED ROBOTS META-LEARNING