Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning.
Guangzhu PengC. L. Philip ChenChenguang YangPublished in: IEEE Trans. Neural Networks Learn. Syst. (2022)
Keyphrases
- mobile robot
- reinforcement learning
- robot control
- autonomous robots
- neural network
- optimal control
- robotic systems
- robot behavior
- real robot
- control policy
- human operators
- control problems
- human robot interaction
- initially unknown
- home environment
- human robot
- mobile robotics
- semi autonomous
- simulated robot
- robot motion
- robot teams
- physical world
- force feedback
- visual feedback
- motion control
- visual servoing
- real time
- control system
- control architecture
- dynamic programming
- indoor environments
- agent learns
- unknown environments
- changing environment
- control signals
- robot navigation
- control loop
- function approximators
- control strategy
- robot manipulators
- robotic arm
- hand eye
- navigation tasks
- autonomous vehicles
- multi agent
- sensory inputs
- learning algorithm
- control method
- action selection
- service robots
- virtual space
- reinforcement learning algorithms
- formation control
- perceptual aliasing
- multi robot
- function approximation
- sensory information
- markov decision processes
- dynamic environments
- motion planning
- human users
- model free