Predictive Learning from Demonstration

Abstract

Abstract.A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.

Behavior recognition for Learning from Demonstration

“…Finally, it backs away from the pile and awaits more instructions. as a controller [21]. PSL is then able to repeat each of the three skills successfully, but unable to reproduce the complete load-transport-unload task.…”

“…This task is selected since the same data has previously been used to evaluate PSL as a controller [21]. In the previous evaluation, it was concluded that the PSL could learn each of the three skills (load, corridor and unload) but PSL was unable to repeat the overall task.…”

“…We believe that this approach has larger potential to provide a generalizable solution to the behavior recognition problem and it also has many interesting connections to neurological models. A longer discussion of these issues can be found in [21].…”

“…A big advantage of using PSL both for control (as described in [21]) and behavior recognition is that the forward and inverse computations are in fact based on the same model, i.e., the PSL library. This approach has several theoretical connections to the view of human perception and control as two heavily intertwined processes.…”

A Formalism for Learning from Demonstration*

“…Action selection where a function π selects a primitive π ∈ Π P : π = π (η ) (16) where π performs the mapping…”