Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation

10/07/2021
by   Eugenio Chisari, et al.
0

Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Deep reinforcement learning algorithms have recently demonstrated impressive results, although they still require an impractical amount of time-consuming trial-and-error iterations. In this work, we consider the promising alternative paradigm of interactive learning where a human teacher provides feedback to the policy during execution, as opposed to imitation learning where a pre-collected dataset of perfect demonstrations is used. Our proposed CEILing (Corrective and Evaluative Interactive Learning) framework combines both corrective and evaluative feedback from the teacher to train a stochastic policy in an asynchronous manner, and employs a dedicated mechanism to trade off human corrections with the robot's own experience. We present results obtained with our framework in extensive simulation and real-world experiments that demonstrate that CEILing can effectively solve complex robot manipulation tasks directly from raw images in less than one hour of real-world training.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 4

page 6

03/24/2022

Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation

Optimizing behaviors for dexterous manipulation has been a longstanding ...
03/13/2020

Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

Imitation learning is an effective and safe technique to train robot pol...
08/25/2019

Combined Task and Action Learning from Human Demonstrations for Mobile Manipulation Applications

Learning from demonstrations is a promising paradigm for transferring kn...
12/05/2020

iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes

We present iGibson, a novel simulation environment to develop robotic so...
03/10/2020

SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks

Recent advances in deep reinforcement learning (RL) have demonstrated it...
09/17/2021

ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning

Effective robot learning often requires online human feedback and interv...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.