No-frills Dynamic Planning using Static Planners

TL;DR

This paper introduces a method that enables static planners to handle dynamic tasks by predicting trajectories and timing, demonstrated through a robotic arm trapping a moving ball.

Contribution

It presents a novel approach combining static planning with trajectory forecasting and timing prediction to solve dynamic environment tasks.

Findings

Successfully generalizes across different environments

Enables static planners to handle moving targets

Demonstrates effectiveness with a robotic arm in dynamic scenarios

Abstract

In this paper, we address the task of interacting with dynamic environments where the changes in the environment are independent of the agent. We study this through the context of trapping a moving ball with a UR5 robotic arm. Our key contribution is an approach to utilize a static planner for dynamic tasks using a Dynamic Planning add-on; that is, if we can successfully solve a task with a static target, then our approach can solve the same task when the target is moving. Our approach has three key components: an off-the-shelf static planner, a trajectory forecasting network, and a network to predict robot's estimated time of arrival at any location. We demonstrate the generalization of our approach across environments. More information and videos at https://mlevy2525.github.io/DynamicAddOn.