Dynamic task execution using active parameter identification with the baxter research robot

Andrew D. Wilson, Jarvis A. Schultz, Alex R. Ansari, Todd D. Murphey

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This paper presents experimental results from the 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, nonlinear 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 the 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.

Original languageEnglish (US)
Article number7582477
Pages (from-to)391-397
Number of pages7
JournalIEEE Transactions on Automation Science and Engineering
Issue number1
StatePublished - Jan 2017


  • Maximum-likelihood estimation
  • Optimal control
  • Parameter estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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