This thesis summary presents research focused on incorporating high-level abstract behavioral requirements, called 'conceptual constraints', into the modeling processes of robot Learning from Demonstration (LfD) techniques. This idea is realized via an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporally associated logical formulae of Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Current work focuses on automating constraint discovery, introducing conceptual constraints into human-aware motion planning algorithms, and expanding upon trajectory alignment techniques for LfD. Future work will focus on how concept constrained algorithms and models are best incorporated into effective interfaces for end-users.
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This content will become publicly available on June 3, 2025
Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study
In this work, we develop an open-source surgical
simulation environment that includes a realistic model obtained
by MRI-scanning a physical phantom, for the purpose of training
and evaluating a Learning from Demonstration (LfD) algorithm
for autonomous suturing. The LfD algorithm utilizes Dynamic
Movement Primitives (DMP) and Locally Weighted Regression
(LWR), but focuses on the needle trajectory, rather than the
instruments, to obtain better generality with respect to needle
grasps. We conduct a user study to collect multiple suturing
demonstrations and perform a comprehensive analysis of the
ability of the LfD algorithm to generalize from a demonstration
at one location in one phantom to different locations in the
same phantom and to a different phantom. Our results indicate
good generalization, on the order of 91.5%, when learning from
more experienced subjects, indicating the need to integrate skill
assessment in the future.
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- Award ID(s):
- 1927275
- PAR ID:
- 10529092
- Publisher / Repository:
- International Symposium on Medical Robotics 2024
- Date Published:
- Format(s):
- Medium: X
- Location:
- Atlanta, GA
- Sponsoring Org:
- National Science Foundation
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