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.
@article{arxiv.1709.03474,
title = {Dynamic Task Execution using Active Parameter Identification with the Baxter Research Robot},
author = {Andrew D. Wilson and Jarvis A. Schultz and Alex R. Ansari and Todd D. Murphey},
journal= {arXiv preprint arXiv:1709.03474},
year = {2017}
}