Search Results for author:
Found 11 papers, 2 papers with code
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states.
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
Disentangled Planning and Control in Vision Based Robotics via Reward Machines
In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies.
From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion
We show that hierarchical policies can concurrently learn to locomote and navigate in these environments, and show they are more efficient than non-hierarchical neural network policies.
Policies Modulating Trajectory Generators
We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.
Data Efficient Reinforcement Learning for Legged Robots
We present a model-based framework for robot locomotion that achieves walking based on only 4. 5 minutes (45, 000 control steps) of data collected on a quadruped robot.
Hierarchical Reinforcement Learning for Quadruped Locomotion
We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-level skills are needed for this task.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Provably Robust Blackbox Optimization for Reinforcement Learning
Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics.
NoRML: No-Reward Meta Learning
To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
The control policies are learned in a physics simulator and then deployed on real robots.
