Using Humanoid Robots to Study Human Behavior
By Christopher G. Atkeson, Joshua G. Hale, Frank E. Pollick, Marcia Riley, S. Kotosaka, Stefan Schaal, T. Shibata, G. Tevatia, Ales Ude, Sethu Vijayakumar and 1 more. IEEE Intelligent Systems, 2000
296 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/d944f2518a72ea65590029c05352e6bc5e6b079c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Jan Peters
- Stefan Schaal
- Peter Pastor
- Sergey Levine
- Heiko Hoffmann
- Auke Jan Ijspeert
- Tamim Asfour
- Aude Gemma Billard
In Cognitive Neuroscience
- Stefan Schaal
- Peter Pastor
- Jan Peters
- Heiko Hoffmann
- Auke Jan Ijspeert
- Ales Ude
- Jun Nakanishi
- Tamim Asfour
In Control and Systems Engineering
- Jan Peters
- Stefan Schaal
- Sergey Levine
- Aude Gemma Billard
- Peter Pastor
- Pieter Abbeel
- Gerhard Neumann
- Tamim Asfour
Citations in the corpus
- Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives (2010, background): “example movement trajectories are stored directly in memory [30], [31].”
- ARMAR-4: A 63 DOF torque controlled humanoid robot (2013, methodology): “This include ASIMO [1], HRP-2 [2], HRP-4C [3], HRP4 [4], Toyota’s partner robot [5], DB [6], CB [7], ARMAR [8], iCub [9], COMAN[10], NAO [11], DARwIn-OP [12], WABIAN2 [13], KOBIAN [14], Twendy-One [15], HUBO [16], Justin [17] and DLR humanoid [18], Lola [19], REEM [20], Robonaut [21], and Petman [22”
- Motor interference between Humans and Humanoid Robots: Effect of Biological and Artificial Motion (2005, methodology): “Recent advances in humanoid robotics [1-3] bring new social issues into consideration.”
- Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors (2013, background): “(2003) and Ijspeert, Nakanishi, and Schaal (2002a) demonsrated imitation learning with a 30 degrees-of-freedom (DOFs) humanoid robot (Atkeson et al., 2000) for performing a tennis forehand, a tennis backhand, and rhythmic drumming movements.”
- Movement imitation with nonlinear dynamical systems in humanoid robots (2002, methodology): “In a last set of experiments, we implemented the system in a humanoid robot with 30 DOFs [ 5 ].”
- Learning Attractor Landscapes for Learning Motor Primitives (2002, background): “9-meter tall 30 DOFs hydraulic anthropomorphic robot with legs, arms, a jointed torso, and a head [9].”
- Learning from observation using primitives (2001, methodology): “The hardware implementation of air hockey, Figure 5, consists of the humanoid robot DB [9], a small air hockey table, and a camera-based tracking system.”