Toward fast policy search for learning legged locomotion
By Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters. 2012
38 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems 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
- Sergey Levine
- Pieter Abbeel
- Chelsea Finn
- Vikash Kumar
- Igor Mordatch
- Anusha Nagabandi
- Abhishek Gupta
- Aravind Rajeswaran
In Biomedical Engineering
- Sergey Levine
- Pieter Abbeel
- Vikash Kumar
- Anusha Nagabandi
- Chelsea Finn
- Ronald S. Fearing
- Gregory Kahn
- Adam Villaflor
In Control and Systems Engineering
- Sergey Levine
- Pieter Abbeel
- Vikash Kumar
- Chelsea Finn
- Abhishek Gupta
- Anusha Nagabandi
- Gregory Kahn
- Igor Mordatch
Citations in the corpus
- Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, methodology): “To the best of our knowledge, no prior model-based method utilizing Gaussian processes has demonstrated successful learning for locomotion tasks with complex contact physics, though several works have proposed to learn the dynamics [26].”
- Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017, background): “In general, model-free algorithms can learn complex tasks [6], [7], but are usually sample-inefficient, while model-based algorithms are typically sample-efficient [8], but have difficulty scaling to complex, high-dimensional tasks [9].”
- Deep Dynamics Models for Learning Dexterous Manipulation (2019, background): “Other work has focused on learning these models using high-capacity function approximators [17, 18, 3, 19, 20, 21, 22] and probabilistic dynamics models [23, 24, 25, 26].”