A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving
By J. Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng, Sebastian Thrun. 2010
75 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Automotive 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
- Jan Peters
- Pieter Abbeel
- Jens Kober
- J. Bagnell
- Chelsea Finn
- Gerhard Neumann
- Vladlen Koltun
In Automotive Engineering
In Control and Systems Engineering
- Jens Kober
- Jan Peters
- J. Bagnell
- Sergey Levine
- Pieter Abbeel
- Jeremy H. Gillula
- Melanie Nicole Zeilinger
- Shahab Kaynama
Citations in the corpus
- Reinforcement learning in robotics: A survey (2013, background): “Similarly, solving a linearized control problem with multiple probabilistic models and combining the resulting closed-loop control with open-loop control has resulted in autonomous sideways sliding into a parking spot (Kolter et al., 2010).”
- A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems (2017, background): “a trained pilot was able to remotely take over control of the autonomous helicopter at any time; the power slide car maneuvers in [5] were performed on an empty test track; and the aerobatic”
- Reachability-based safe learning with Gaussian processes (2014, background): “Examples include rotorcraft performing aggressive maneuvers with high airflow interaction [1],[5], autonomous driving in extreme conditions [13] or fast quadruped locomotion through irregular terrain [10].”