Stochastic Action Prediction for Imitation Learning

12/26/2020
by   Sagar Gubbi Venkatesh, et al.
0

Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inherent stochasticity in demonstrations collected for tasks including line following with a remote-controlled car and manipulation tasks including reaching, pushing, and picking and placing an object. We model stochasticity in the data distribution using autoregressive action generation, generative adversarial nets, and variational prediction and compare the performance of these approaches. We find that accounting for stochasticity in the expert data leads to substantial improvement in the success rate of task completion.

READ FULL TEXT
research
06/17/2021

CRIL: Continual Robot Imitation Learning via Generative and Prediction Model

Imitation learning (IL) algorithms have shown promising results for robo...
research
05/29/2019

Adversarial Imitation Learning from Incomplete Demonstrations

Imitation learning targets deriving a mapping from states to actions, a....
research
05/16/2020

Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning

The current Air Traffic Management (ATM) system worldwide has reached it...
research
10/03/2018

Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation

Human hand actions are quite complex, especially when they involve objec...
research
12/26/2020

Multi-Instance Aware Localization for End-to-End Imitation Learning

Existing architectures for imitation learning using image-to-action poli...
research
05/22/2022

Chain of Thought Imitation with Procedure Cloning

Imitation learning aims to extract high-performance policies from logged...
research
04/06/2023

End-to-end Manipulator Calligraphy Planning via Variational Imitation Learning

Planning from demonstrations has shown promising results with the advanc...

Please sign up or login with your details

Forgot password? Click here to reset