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Found 5 papers, 2 papers with code
Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise
The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics
We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem.
RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization
The previous work on such learning RRTs use direct optimal control to generate the data for supervised learning.
Robotics Optimization and Control
Team Delft's Robot Winner of the Amazon Picking Challenge 2016
This paper describes Team Delft's robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions.
Robotics
