  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.
