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Lynne E. Parker

5 accepted papers

2017

Minimum uncertainty latent variable models for robot recognition of sequential human activities

ICRA 2017poster

Recognition of sequential human activities, such as “sitting down” and “standing up”, is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCR…

Cited by 4SourceScholar
2016

SRAC: Self-Reflective Risk-Aware Artificial Cognitive models for robot response to human activities

ICRA 2016

In human-robot teaming, interpretation of human actions, recognition of new situations, and appropriate decision making are crucial abilities for cooperative robots (“co-robots”) to interact intelligently with humans. Given an observation, it is important that human activities are interpreted the sa

Cited by 3SourceScholar
2015

Adaptive human-centered representation for activity recognition of multiple individuals from 3D point cloud sequences

ICRA 2015poster

Activity recognition of multi-individuals (ARMI) within a group, which is essential to practical human-centered robotics applications such as childhood education, is a particularly challenging and previously not well studied problem. We present a novel adaptive human-centered (AdHuC) representation…

Cited by 15SourceScholar
2015

Bio-inspired predictive orientation decomposition of skeleton trajectories for real-time human activity prediction

ICRA 2015poster

Activity prediction is an essential task in practical human-centered robotics applications, such as security, assisted living, etc., which targets at inferring ongoing human activities based on incomplete observations. To address this challenging problem, we introduce a novel bio-inspired predictive…

Cited by 33SourceScholar