Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

Nina Moorman; Julianna Schalkwyk; Vriksha Srihari; Qingyu Xiao; Kamel Alrashedy; Hongseok Jeong; Kiersten Lange; Matthew B. Luebbers

cs.ROAug 25, 2026

Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

Authors: Nina Moorman, Julianna Schalkwyk, Vriksha Srihari, Qingyu Xiao, Kamel Alrashedy, Hongseok Jeong, Kiersten Lange, Matthew B. Luebbers,

Organizations: Georgia Institute of Technology, Atlanta, GA, USA

Abstract

Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. In this work we identify non-expert end users' key barriers when teaching robots via demonstration without live robotics expert feedback in a home environment. In our human subjects experiment we support the non-expert end users through two forms of demonstrator guidance developed in prior work: pre-training and adaptive feedback. Towards the ecological validity of the evaluation, we conduct this experimentation over multiple visits, with a population of care providers. Finally, we propose to open source the resulting LfD dataset of care providers teaching a robot assistive tasks over multiple visits to a home environment.

Figures & tables

Fig. 1: Domains for visit 1 (first row) and visit 2 (second row). Tupperware stacking serves as a kinesthetic on-boarding task for both visits. These domains were chosen to represent a range of activities of daily living. Additionally, we balance task complexity and distribution of unimanual to bimanual tasks between visits.

Fig. 2: This figure depicts the dataset structure where each datapoint represents one visit, with information about the participant, domains, tasks, and demonstrated skills.

Learning from Demonstration

Learning from Demonstration teaches agents and robots new behaviors by extracting actions, goals, and task structure from examples.

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Jun 15, 2026J/K move · Enter open · S save

LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations

Johannes Heidersberger, Shail Jadav, Dongheui Lee

Autonomous Systems Lab, Institute of Computer Technology, TU Wien, Vienna, Austria · Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Weßling, Germany

Robot Skill Learning Active Learning Learning from Demonstration Robot Imitation Learning

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Learning from Demonstration (LfD) enables intuitive robot skill acquisition by allowing robots to learn directly from human task demonstrations. However, current methods often fail to address the fact that due to suboptimal and inconsistent human behavior, the quality of the demonstration can vary within each demonstration. Therefore, we introduce LOPAL (LOcal Performance-aware Active Learning), an active learning approach that leverages this local demonstration quality information. Our approach consists of two synergistic components. First, a local performance-driven LfD method uses a Gaussian Mixture Model (GMM) to encode both the demonstrated trajectories and their associated local quality assessments. This enables the generation of trajectories that outperform the imperfect demonstrations by utilizing complementary local data of high performance. Second, active data acquisition allows to improve beyond the imperfect demonstrations by collecting additional informative samples. In areas missing good data, the user is actively requested to provide corrections through a shared autonomy (SA) mechanism, while the robot autonomously executes the learned behavior. The efficacy of LOPAL was validated in both a simulation and a real-world experiment. The results from a real-world pipe inspection task showed that the proposed approach can achieve up to 27.31 % improvement in task performance while also reducing the effort required to collect the demonstrations.

Abstract

Learning from Demonstration

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