Robotic Interactive Physics Parameters Estimator (RIPPE)

Abstract

The ability to reason about natural laws of an environment directly contributes to successful performance in it. In this work, we present RIPPE, a framework that allows a robot to leverage existing physics simulators as its knowledge base for learning interactions with in-animate objects. To achieve this, the robot needs to initially interact with its surrounding environment and observe the effects of its behaviours. Relying on the simulator to efficiently solve the partial differential equations describing these physical interactions, the robot infers consistent physical parameters of its surroundings by repeating the same actions in simulation and evaluate how closely they match its real observations. The learning process is performed using Bayesian Optimisation techniques to sample efficiently the parameter space. We assess the utility of these inferred parameters by measuring how well they can explain physical interactions using previously unseen actions and tools.