10 research outputs found
Integration of Riemannian Motion Policy and Whole-Body Control for Dynamic Legged Locomotion
In this paper, we present a novel Riemannian Motion Policy (RMP)flow-based
whole-body control framework for improved dynamic legged locomotion. RMPflow is
a differential geometry-inspired algorithm for fusing multiple task-space
policies (RMPs) into a configuration space policy in a geometrically consistent
manner. RMP-based approaches are especially suited for designing simultaneous
tracking and collision avoidance behaviors and have been successfully deployed
on serial manipulators. However, one caveat of RMPflow is that it is designed
with fully actuated systems in mind. In this work, we, for the first time,
extend it to the domain of dynamic-legged systems, which have unforgiving
under-actuation and limited control input. Thorough push recovery experiments
are conducted in simulation to validate the overall framework. We show that
expanding the valid stepping region with an RMP-based collision-avoidance swing
leg controller improves balance robustness against external disturbances by up
to compared to a baseline approach using a restricted stepping region.
Furthermore, a point-foot biped robot is purpose-built for experimental studies
of dynamic biped locomotion. A preliminary unassisted in-place stepping
experiment is conducted to show the viability of the control framework and
hardware
Learning Stable Robotic Skills on Riemannian Manifolds
In this paper, we propose an approach to learn stable dynamical systems
evolving on Riemannian manifolds. The approach leverages a data-efficient
procedure to learn a diffeomorphic transformation that maps simple stable
dynamical systems onto complex robotic skills. By exploiting mathematical tools
from differential geometry, the method ensures that the learned skills fulfill
the geometric constraints imposed by the underlying manifolds, such as unit
quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices
for impedance, while preserving the convergence to a given target. The proposed
approach is firstly tested in simulation on a public benchmark, obtained by
projecting Cartesian data into UQ and SPD manifolds, and compared with existing
approaches. Apart from evaluating the approach on a public benchmark, several
experiments were performed on a real robot performing bottle stacking in
different conditions and a drilling task in cooperation with a human operator.
The evaluation shows promising results in terms of learning accuracy and task
adaptation capabilities.Comment: 16 pages, 10 figures, journa
Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning
This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases