From human to humanoid locomotion—an inverse optimal control approach

471 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications (2017, background): “7) Inverse Optimal Control: Inverse optimal control problems are essentially bilevel in nature [6], [88], [122], [158] with wide applica-”
Socially compliant mobile robot navigation via inverse reinforcement learning (2016, background): “Pedestrians typically aim to walk efficiently, avoiding unnecessary accelerations (Hoogendoorn and Bovy, 2003; Mombaur et al., 2010).”
Segmenting sign language into motor primitives with Bayesian binning (2013, background): “locomotion and arm reaching) were shown to be well captured by minimum acceleration models (Ben-Itzhak and Karniel, 2008; Mombaur et al., 2010).”
Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models (2017, methodology): “Inverse Reinforcement Learning (IRL) [41, 32, 2, 29, 38, 50, 16] is a powerful and flexible framework for tackling these challenges and has been previously used for tasks such as modeling and mimicking human driver behavior [1, 28, 43], pedestrian trajectory prediction [51, 31], and legged robot loc”