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Found 15 papers, 7 papers with code
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots
In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt).
Rapid Exploration for Open-World Navigation with Latent Goal Models
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.
LaND: Learning to Navigate from Disengagements
However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate.
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal.
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots.
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs.
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
We show that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time be able to accomplish a variety of different tasks.
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
We present an approach for controlling a real-world legged millirobot that is based on learned neural network models.
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based.
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot.
PLATO: Policy Learning using Adaptive Trajectory Optimization
PLATO also maintains the MPC cost as an objective to avoid highly undesirable actions that would result from strictly following the learned policy before it has been fully trained.
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment.
