Automated Design of Adaptive Controllers for Modular Robots using Reinforcement Learning
By Paulina Varshavskaya, Leslie Pack Kaelbling, Daniela Rus. 2008
53 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanical Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lei Tai
- Daniela Rus
- Leslie Pack Kaelbling
- Paulina Varshavskaya
- Sergey Levine
- Josh Tenenbaum
- Tomas Lozano-Perez
- Davood Hajinezhad
In Control and Systems Engineering
In Mechanical Engineering
- Lei Tai
- Leslie Pack Kaelbling
- Paulina Varshavskaya
- Daniela Rus
- Davood Hajinezhad
- Afshin Oroojlooyjadid
Citations in the corpus
- A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “ch as inverted helicopter flight learning (Kim et al., 2003; Ng et al., 2006), gliding aircraft control (Chung et al., 2015), mobile robot navigation (Kretzschmar et al., 2016), modular robot control (Varshavskaya et al., 2008) and biped walking (Benbrahim and Franklin, 1997). For a standard reinforc”
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, background): “…learning, such as inverted helicopter flight learning (Kim et al., 2003; Ng et al., 2006), gliding aircraft control (Chung et al., 2015), mobile robot navigation (Kretzschmar et al., 2016), modular robot control (Varshavskaya et al., 2008) and biped walking (Benbrahim and Franklin, 1997).”
- A Concise Introduction to Decentralized POMDPs (2016, unknown): “This approach was extended to learn policies for self-configurable modular robots [Varshavskaya et al., 2008].”
- Efficient Distributed Reinforcement Learning through Agreement (2008, methodology): “5), and the original R is the arithmetic mean of local Ri’s, such as when SRMR modules learn a locomotion policy [16].”