@inproceedings{5a3edb88517b45b984d9dec90177a961,
title = "Real-time trajectory synthesis for information maximization using Sequential Action Control and least-squares estimation",
abstract = "This paper presents the details and experimental results from an implementation of real-time trajectory generation and parameter estimation of a dynamic model using the Baxter Research Robot from Rethink Robotics. Trajectory generation is based on the maximization of Fisher information in real-time and closed-loop using a form of Sequential Action Control. On-line estimation is performed with a least-squares estimator employing a nonlinear state observer model computed with trep, a dynamics simulation package. 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. Several trials are presented with varying initial estimates showing convergence to the actual length within a 6 second time-frame.",
author = "Wilson, {Andrew D.} and Jarvis Schultz and Ansari, {Alex R.} and Murphey, {Todd David}",
year = "2015",
month = dec,
day = "11",
doi = "10.1109/IROS.2015.7354071",
language = "English (US)",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4935--4940",
booktitle = "IROS Hamburg 2015 - Conference Digest",
address = "United States",
note = "IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 ; Conference date: 28-09-2015 Through 02-10-2015",
}