Learning Task Constraints in Visual-Action Planning from Demonstrations

Abstract

Visual planning approaches have shown great success for decision making tasks with no explicit model of the state space. Learning a suitable representation and constructing a latent space where planning can be performed allows non-experts to setup and plan motions by just providing images. However, learned latent spaces are usually not semantically-interpretable, and thus it is difficult to integrate task constraints. We propose a novel framework to determine whether plans satisfy constraints given demonstrations of policies that satisfy or violate the constraints. The demonstrations are realizations of Linear Temporal Logic formulas which are employed to train Long Short-Term Memory (LSTM) networks directly in the latent space representation. We demonstrate that our architecture enables designers to easily specify, compose and integrate task constraints and achieves high performance in terms of accuracy. Furthermore, this visual planning framework enables human interaction, coping the environment changes that a human worker may involve. We show the flexibility of the method on a box pushing task in a simulated warehouse setting with different task constraints.

Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations

“…One common approach for extracting knowledge from demonstrations is policy learning, where the goal is to learn a policy that maps the input space into the output space (usually the state and action spaces, respectively) and that can accurately mimic the demonstrations. Some approaches bypass explicit state characterization by defining as input raw data from the system with deep learning methods [13], [14]. Another approach that is widely used is based on Markov Decision Processes.…”