| | |
| | |
Stat |
Members: 3645 Articles: 2'506'133 Articles rated: 2609
26 April 2024 |
|
| | | |
|
Article overview
| |
|
Policies Modulating Trajectory Generators | Atil Iscen
; Ken Caluwaerts
; Jie Tan
; Tingnan Zhang
; Erwin Coumans
; Vikas Sindhwani
; Vincent Vanhoucke
; | Date: |
7 Oct 2019 | Abstract: | We propose an architecture for learning complex controllable behaviors by
having simple Policies Modulate Trajectory Generators (PMTG), a powerful
combination that can provide both memory and prior knowledge to the controller.
The result is a flexible architecture that is applicable to a class of problems
with periodic motion for which one has an insight into the class of
trajectories that might lead to a desired behavior. We illustrate the basics of
our architecture using a synthetic control problem, then go on to learn
speed-controlled locomotion for a quadrupedal robot by using Deep Reinforcement
Learning and Evolutionary Strategies. We demonstrate that a simple linear
policy, when paired with a parametric Trajectory Generator for quadrupedal
gaits, can induce walking behaviors with controllable speed from 4-dimensional
IMU observations alone, and can be learned in under 1000 rollouts. We also
transfer these policies to a real robot and show locomotion with controllable
forward velocity. | Source: | arXiv, 1910.2812 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |