Dynamic Task Execution using Active Parameter Identification with the Baxter Research Robot

TL;DR

This paper demonstrates real-time parameter estimation and trajectory optimization on the Baxter robot for a dynamic task involving a suspended load, highlighting the importance of active estimation for successful task execution.

Contribution

It introduces an active parameter estimation method using Fisher information and integrates it with trajectory optimization for dynamic manipulation tasks on Baxter.

Findings

Active estimation improves trajectory accuracy.

Real-time parameter estimation enables successful load swinging.

Experimental validation with multiple trials shows effectiveness.

Abstract

This paper presents experimental results from real-time parameter estimation of a system model and subsequent trajectory optimization for a dynamic task using the Baxter Research Robot from Rethink Robotics. An active estimator maximizing Fisher information is used in real-time with a closed-loop, non-linear control technique known as Sequential Action Control. Baxter is tasked with estimating the length of a string connected to a load suspended from the gripper with a load cell providing the single source of feedback to the estimator. Following the active estimation, a trajectory is generated using the trep software package that controls Baxter to dynamically swing a suspended load into a box. Several trials are presented with varying initial estimates showing that estimation is required to obtain adequate open-loop trajectories to complete the prescribed task. The result of one trial with and without the active estimation is also shown in the accompanying video.