Thesis Defense: Robust Human Motion Prediction for Safe and Efficient Human-Robot Interaction
Przemyslaw (Pem) Lasota
, AeroAstro / CSAIL
Date: Friday, May 17, 2019
Time: 10:00 AM to 12:00 PM
Event Type: Thesis Defense
Room Description: Seminar Room D463 (Star)
Host: Julie Shah
Contact: Julie A Shah, 617-324-4879, firstname.lastname@example.org
Speaker URL: None
TALK: Thesis Defense: Robust Human Motion Prediction for Safe and Efficient Human-Robot Interaction
Location: D463 (Star)
From robotic co-workers in factories to assistive robots in homes, human-robot interaction (HRI) has the potential to revolutionize a large array of domains by enabling robotic assistance where it was previously not possible. Introducing robots into human-occupied domains, however, requires strong consideration for the safety and efficiency of the interaction. One particularly effective method of supporting safe and efficient HRI is through the use of human motion prediction. Current approaches, however, often lack the robustness required for real-world deployment. Many methods are designed for predicting specific types of tasks and motions, and do not necessarily generalize well to other domains. Another key limitation lies in deficiencies in partial trajectory alignment, which is a key enabling technology for many goal-based prediction methods.
In this thesis, I introduce two frameworks designed to improve the robustness of human motion prediction in order to facilitate its use for safe and efficient human-robot interaction. First, I introduce the Multiple-Predictor System (MPS), a data-driven approach that uses given task and motion data in order to synthesize a high performing predictor by automatically identifying informative prediction features and combining the strengths of complementary prediction methods. With the use of three distinct human motion datasets, I show that using the MPS leads to lower prediction error in a variety of HRI scenarios.
Second, in order to address the drawbacks of prior alignment techniques, I introduce the Bayesian ESTimator for Partial Trajectory Alignment (BEST-PTA). This framework uses a combination of optimization, supervised learning, and unsupervised learning components that are trained and synthesized based on a given set of example trajectories. Through an evaluation on three human motion datasets, I show that BEST-PTA reduces alignment error when compared to state-of-the-art baselines. Furthermore, I demonstrate that this improved alignment reduces human motion prediction error.
Lastly, in order to assess the utility of the developed methods for improving safety and efficiency in HRI, I introduce an integrated framework combining prediction with robot planning in time. I describe an implementation and evaluation of this framework on a real physical system and show that the developed approach leads to automatically derived adaptive robot behavior. I also demonstrate that the developed framework leads to improvements in quantitative metrics of safety and efficiency with the use of simulated evaluations
Created by Julie A Shah at Monday, May 13, 2019 at 7:51 PM.