Search Results for author: Gregory Kahn

Found 15 papers, 7 papers with code

Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots

no code implementations24 Jun 2021 Katie Kang, Gregory Kahn, Sergey Levine

In this work, we propose a deep reinforcement learning algorithm with hierarchically integrated models (HInt).

Navigate reinforcement-learning +1

Rapid Exploration for Open-World Navigation with Latent Goal Models

no code implementations12 Apr 2021 Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine

We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.

Autonomous Navigation

ViNG: Learning Open-World Navigation with Visual Goals

no code implementations17 Dec 2020 Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine

We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.

Navigate reinforcement-learning +1

LaND: Learning to Navigate from Disengagements

1 code implementation9 Oct 2020 Gregory Kahn, Pieter Abbeel, Sergey Levine

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.

Autonomous Navigation Imitation Learning +3

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

2 code implementations23 Apr 2020 Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan Mcallister, Roberto Calandra, Sergey Levine

Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks.

Meta-Learning Meta Reinforcement Learning +2

BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

1 code implementation13 Feb 2020 Gregory Kahn, Pieter Abbeel, Sergey Levine

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.

Navigate Robot Navigation +1

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

no code implementations27 Dec 2018 Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine

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

1 code implementation16 Oct 2018 Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine

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.

Robot Navigation

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

no code implementations14 Nov 2017 Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. Fearing

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

2 code implementations29 Sep 2017 Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine

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.

Navigate Q-Learning +3

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

8 code implementations8 Aug 2017 Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine

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.

Model-based Reinforcement Learning Model Predictive Control +2

Uncertainty-Aware Reinforcement Learning for Collision Avoidance

no code implementations3 Feb 2017 Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, Sergey Levine

However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot.

Collision Avoidance Navigate +2

PLATO: Policy Learning using Adaptive Trajectory Optimization

no code implementations2 Mar 2016 Gregory Kahn, Tianhao Zhang, Sergey Levine, Pieter Abbeel

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.

Model Predictive Control

Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

no code implementations22 Sep 2015 Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel

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.

Model Predictive Control reinforcement-learning +1

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