Curiosity-Based Learning Algorithm for distributed interactive sculptural systems

The ability to engage human observers is a key requirement for both social robots and the arts. In this paper, we propose an approach for adapting the Intelligent Adaptive Curiosity learning algorithm to distributed interactive sculptural systems. This Curiosity-Based Learning Algorithm (CBLA) allows the system to learn about its own mechanisms and its surroundings through self-experimentation and interaction. A novel formulation using multiple agents as learning subsets of the system that communicate through shared input variables enables us to scale to a much larger system with diverse types of sensors and actuators. Experiments on a prototype interactive sculpture demonstrate the exploratory patterns of the CBLA and collective learning behaviours through the integration of multiple learning agents.

Authors

• Matthew T. K. Chan
• Rob Gorbet
• Philip Beesley
• Dana Kulic

Keywords

• Robot sensing systems
• Actuators
• Art
• Robot kinematics
• Context
• Learning Algorithms
• Interactive System
• Input Variables
• Multiple Agents
• Social Robots
• Actual Values
• Prediction Error
• Accelerometer
• Distribution System
• Output Variables
• Sensory Input
• Regional Model
• Shape Memory
• Cut Value
• Learning Progress
• Reinforcement Learning Algorithm
• Tentacles
• Sensor Inputs
• Random Action
• Number Of Actuators
• Idle Mode
• Protocells
• High-level Layers
• Prediction Model
• Learning Process
• Time Step
• Proprioceptive
• robots and embodied art
• reinforcement learning
• intrinsic motivation