Kyle Lockwood PhD Dissertation Defense

Name:
Kyle Lockwood

Title:
Leveraging Submovements for Prediction and Trajectory Planning in  Human-Robot Handover

Date:
7/30/2024

Time:
1:30:00 PM

Location:
ISEC 532 –

Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Eugene Tunik (Co-Advisor)
Prof. Mathew Yarossi
Prof. Tales Imbiriba

Abstract:
Collaborative physical interactions between humans and robots pose difficult modeling challenges. To create natural interactions, engineers must consider human inference of intent, anticipation of action, and coordination of movement. Humans can handle these challenges effortlessly when interacting with one another, but they are very difficult to overcome in robot implementations. Although human-human handover is a seemingly simple task, it requires a complex perception-action coupling to determine when and where the handover will happen, as well as choosing an appropriate trajectory to receive the object. Critically, modeling human-robot handover requires incorporating knowledge about human inference and trajectory planning to obtain seamless interactions. Despite recent advancements in sensing and control, human-robot handovers are far from approaching the fluidity and flexibility of human-human collaboration. Existing predictive models applied to human-robot handover often utilize classification methods and other approaches that suffer in accuracy when encountering noisy human trajectories that are not captured during their training. To address these challenges, this work presents two models that act as robotic surrogates for human inference and trajectory planning in a handover task. This approach delivers promising results while remaining grounded in a physiologically meaningful feature of human motion: Gaussian-shaped submovements in velocity profiles. This thesis analyzes human-human handover kinematics to establish a baseline for model evaluation and investigate the influence of handover role, it presents models for human inference and trajectory planning, and it applies the inference model in human-robot handover experiments.