scholarly journals An Algorithmic Perspective on Imitation Learning

2018 ◽  
Vol 7 (1-2) ◽  
pp. 1-179 ◽  
Author(s):  
Takayuki Osa ◽  
Joni Pajarinen ◽  
Gerhard Neumann ◽  
J. Andrew Bagnell ◽  
Pieter Abbeel ◽  
...  
Keyword(s):  
Imitation Learning

Robot Imitation Learning of High-Level Planning Information

2005 ◽  
Author(s):  
Frderick L. Crabbe ◽  
Rebecca Hwa
Keyword(s):  
Imitation Learning ◽  
High Level

scholarly journals Imitation learning-based framework for learning 6-D linear compliant motions

2021 ◽  
Author(s):  
Markku Suomalainen ◽  
Fares J. Abu-dakka ◽  
Ville Kyrki
Keyword(s):  
Contact Force ◽  
Free Space ◽  
Prior Information ◽  
Goal Area ◽  
Imitation Learning ◽  
Learning From Demonstration ◽  
End Effector ◽  
Localization Accuracy ◽  
Novel Method ◽  
Contact Tasks

AbstractWe present a novel method for learning from demonstration 6-D tasks that can be modeled as a sequence of linear motions and compliances. The focus of this paper is the learning of a single linear primitive, many of which can be sequenced to perform more complex tasks. The presented method learns from demonstrations how to take advantage of mechanical gradients in in-contact tasks, such as assembly, both for translations and rotations, without any prior information. The method assumes there exists a desired linear direction in 6-D which, if followed by the manipulator, leads the robot’s end-effector to the goal area shown in the demonstration, either in free space or by leveraging contact through compliance. First, demonstrations are gathered where the teacher explicitly shows the robot how the mechanical gradients can be used as guidance towards the goal. From the demonstrations, a set of directions is computed which would result in the observed motion at each timestep during a demonstration of a single primitive. By observing which direction is included in all these sets, we find a single desired direction which can reproduce the demonstrated motion. Finding the number of compliant axes and their directions in both rotation and translation is based on the assumption that in the presence of a desired direction of motion, all other observed motion is caused by the contact force of the environment, signalling the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test setups imitating typical tasks where a human would use compliance to cope with positional uncertainty. Results show that the method can successfully learn and reproduce compliant motions by taking advantage of the geometry of the task, therefore reducing the need for localization accuracy.

scholarly journals UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning

2021 ◽  
pp. 1-1
Author(s):  
Alireza Shamsoshoara ◽  
Fatemeh Afghah ◽  
Erik Blasch ◽  
Jonathan Ashdown ◽  
Mehdi Bennis
Keyword(s):  
Imitation Learning

scholarly journals Imitation Learning for improved 3D PET/MR attenuation correction

2021 ◽  
pp. 102079
Author(s):  
Kerstin Kläser ◽  
Thomas Varsavsky ◽  
Pawel Markiewicz ◽  
Tom Vercauteren ◽  
Alexander Hammers ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Imitation Learning ◽  
3D Pet

Imitation Learning-based Algorithm for Drone Cinematography System

2020 ◽  
pp. 1-1
Author(s):  
Yuanjie Dang ◽  
Chong Huang ◽  
Peng Chen ◽  
Ronghua Liang ◽  
Xin Yang ◽  
...  
Keyword(s):  
Imitation Learning

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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