Legged Robots
14 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Legged Robots
Most implemented papers
Meta Learning Shared Hierarchies
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.
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment.
Learning agile and dynamic motor skills for legged robots
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.
Reset-free Trial-and-Error Learning for Robot Damage Recovery
resibots/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.
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
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.
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
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.
SGD for robot motion? The effectiveness of stochastic optimization on a new benchmark for biped locomotion tasks
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.
Learning Fast Adaptation with Meta Strategy Optimization
The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
malteschilling/ddrl_hexapod
•
•
Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots.
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
csiro-robotics/deep-terrain-classification
•
•
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ.
