9 research outputs found
Robot Learning from Demonstration Using Elastic Maps
Learning from Demonstration (LfD) is a popular method of reproducing and
generalizing robot skills from human-provided demonstrations. In this paper, we
propose a novel optimization-based LfD method that encodes demonstrations as
elastic maps. An elastic map is a graph of nodes connected through a mesh of
springs. We build a skill model by fitting an elastic map to the set of
demonstrations. The formulated optimization problem in our approach includes
three objectives with natural and physical interpretations. The main term
rewards the mean squared error in the Cartesian coordinate. The second term
penalizes the non-equidistant distribution of points resulting in the optimum
total length of the trajectory. The third term rewards smoothness while
penalizing nonlinearity. These quadratic objectives form a convex problem that
can be solved efficiently with local optimizers. We examine nine methods for
constructing and weighting the elastic maps and study their performance in
robotic tasks. We also evaluate the proposed method in several simulated and
real-world experiments using a UR5e manipulator arm, and compare it to other
LfD approaches to demonstrate its benefits and flexibility across a variety of
metrics.Comment: 7 pages, 9 figures, 3 tables. Accepted to IROS 2022. Code available
at: https://github.com/brenhertel/ElMapTrajectories Accompanying video at:
https://youtu.be/rZgN9Pkw0t
DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1
This handbook outlines all test methods developed under the Development and
Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle
Evaluations (DECISIVE) project by the University of Massachusetts Lowell for
evaluating small unmanned aerial systems (sUAS) performance in subterranean and
constrained indoor environments, spanning communications, field readiness,
interface, obstacle avoidance, navigation, mapping, autonomy, trust, and
situation awareness. For sUAS deployment in subterranean and constrained indoor
environments, this puts forth two assumptions about applicable sUAS to be
evaluated using these test methods: (1) able to operate without access to GPS
signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in)
wide (i.e., can physically fit through a typical doorway, although successful
navigation through is not guaranteed). All test methods are specified using a
common format: Purpose, Summary of Test Method, Apparatus and Artifacts,
Equipment, Metrics, Procedure, and Example Data. All test methods are designed
to be run in real-world environments (e.g., MOUT sites) or using fabricated
apparatuses (e.g., test bays built from wood, or contained inside of one or
more shipping containers).Comment: Approved for public release: PAO #PR2022_4705
DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I
This report reviews all results derived from performance benchmarking
conducted during Phase I of the Development and Execution of Comprehensive and
Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by
the University of Massachusetts Lowell, using the test methods specified in the
DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial
systems (sUAS) performance in subterranean and constrained indoor environments,
spanning communications, field readiness, interface, obstacle avoidance,
navigation, mapping, autonomy, trust, and situation awareness. Using those 20
test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo
Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2
GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden
Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for
each applicable test method and the sUAS that match this criteria are named for
each test method, including a high-level executive summary of their
performance.Comment: Approved for public release: PAO #PR2023_74172; arXiv admin note:
substantial text overlap with arXiv:2211.0180
Confidence-Based Skill Reproduction Through Perturbation Analysis
Several methods exist for teaching robots, with one of the most prominent
being Learning from Demonstration (LfD). Many LfD representations can be
formulated as constrained optimization problems. We propose a novel convex
formulation of the LfD problem represented as elastic maps, which models
reproductions as a series of connected springs. Relying on the properties of
strong duality and perturbation analysis of the constrained optimization
problem, we create a confidence metric. Our method allows the demonstrated
skill to be reproduced with varying confidence level yielding different levels
of smoothness and flexibility. Our confidence-based method provides
reproductions of the skill that perform better for a given set of constraints.
By analyzing the constraints, our method can also remove unnecessary
constraints. We validate our approach using several simulated and real-world
experiments using a Jaco2 7DOF manipulator arm.Comment: 7 pages, 5 figures. Accepted to UR 2023. Code available at
https://github.com/brenhertel/LfD-Perturbations Accompanying video at:
https://youtu.be/IQDxbhEiNb