Search Results for author: Nathan Ratliff

Found 10 papers, 3 papers with code

Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories

no code implementations7 May 2021 Mandy Xie, Anqi Li, Karl Van Wyk, Frank Dellaert, Byron Boots, Nathan Ratliff

Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distributional shift by interacting with oracular experts.

Imitation Learning

RMP2: A Structured Composable Policy Class for Robot Learning

no code implementations10 Mar 2021 Anqi Li, Ching-An Cheng, M. Asif Rana, Man Xie, Karl Van Wyk, Nathan Ratliff, Byron Boots

Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots.

Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees

no code implementations24 Dec 2020 M. Asif Rana, Anqi Li, Dieter Fox, Sonia Chernova, Byron Boots, Nathan Ratliff

The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned.

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems

2 code implementations L4DC 2020 Muhammad Asif Rana, Anqi Li, Dieter Fox, Byron Boots, Fabio Ramos, Nathan Ratliff

The complex motions are encoded as rollouts of a stable dynamical system, which, under a change of coordinates defined by a diffeomorphism, is equivalent to a simple, hand-specified dynamical system.

Density Estimation

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning

no code implementations21 May 2020 Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox

Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.

DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System

no code implementations7 Oct 2019 Ankur Handa, Karl Van Wyk, Wei Yang, Jacky Liang, Yu-Wei Chao, Qian Wan, Stan Birchfield, Nathan Ratliff, Dieter Fox

Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping

no code implementations7 Oct 2019 Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan Ratliff

RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment.

Imitation Learning

RMPflow: A Computational Graph for Automatic Motion Policy Generation

1 code implementation16 Nov 2018 Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan Ratliff

We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs).

Robotics Systems and Control

Predictor-Corrector Policy Optimization

1 code implementation15 Oct 2018 Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning.

Imitation Learning online learning

Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

no code implementations12 Oct 2018 Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, Dieter Fox

In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world.

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