Apprenticeship learning for helicopter control
By Adam Coates, Pieter Abbeel, Andrew Y. Ng. 2009
107 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Jan Peters
- J. Bagnell
- Jens Kober
- Chelsea Finn
- Peter Stone
- J. Urgen Schmidhuber
In Computer Vision and Pattern Recognition
- Jan Peters
- Jens Kober
- Andrew Bagnell
- March
- Prashant Doshi
- Saurabh Arora
- Paolo Traverso
- Satinder Singh
Citations in the corpus
- Reinforcement learning in robotics: A survey (2013, background): “…learning within robotics centers on the use of classical optimal control techniques (e.g. linear-quadratic regulation and differential dynamic programming (DDP)) to system models learned via repeated interaction with the environment (Atkeson, 1998; Bagnell and Schneider, 2001; Coates et al., 2009).”
- Towards Resolving Unidentifiability in Inverse Reinforcement Learning (2016, background): “IRL has seen a number of applications in the development of autonomous systems, such as autonomous vehicle operation, where even a cooperative (human) agent might have great difficultly describing her incentives [17, 2, 1, 5].”
- Deliberation for autonomous robots: A survey (2017, background): “• Sensory-motor control models, as in [47, 121, 120, 164], and most of the work surveyed in [197];”
- Automated Planning and Acting (2016, background): “This form of learning through teleoperation, where the teacher acts directly in the actuators and proprioceptive sensor spaces of the robot, is quite successful (see [537, 475] and the survey of [24]), including for quite complex tasks such as helicopter acrobatics [2, 125].”
- An Invitation to Imitation (2015, background): “Similarly, the works of [Atkeson and Schaal, 1997 ], [Kober and Peters, 2010 ] and [Coates et al., 2009 ] use exactly same kind of benefits to achieve impressive performance.”
- A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress (2018, methodology): “apply IRL to the resultant noise-free trajectories [48], using apprenticeship learning.”