Joint action understanding improves robot-to-human object handover

By Elena Corina Grigore, K. Eder, A. Pipe, Chris Melhuish, U. Leonards. 2013

105 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Social Psychology, Reinforcement Learning, Robotics & Control

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Wee Sun Lee
2. David Hsu
3. Maya Cakmak
4. Owen Macindoe
5. Siddhartha S. Srinivasa
6. Chelsea Finn
7. James Harrison
8. Gerhard Neumann

In Control and Systems Engineering

1. David Hsu
2. Wee Sun Lee
3. Andras Gabor Kupcsik
4. Maya Cakmak
5. Gerhard Neumann
6. Riad Akrour

In Social Psychology

1. Bilge Mutlu
2. Maya Cakmak

In Reinforcement Learning

1. David Hsu
2. Wee Sun Lee
3. Andras Gabor Kupcsik
4. Maya Cakmak
5. Gerhard Neumann
6. Riad Akrour

In Robotics & Control

1. David Hsu
2. Bilge Mutlu
3. Chien-Ming Huang
4. Andras Gabor Kupcsik
5. Maya Cakmak

Citations in the corpus

• Learning Dynamic Robot-to-Human Object Handover from Human Feedback (2016, background): “In the second stage, the giver and taker signal to each other and exchange information, often through non-verbal communication, such as motion [12], eye gaze, or head orientation [13], in order to establish joint intention of handover.”
• Adaptive Coordination Strategies for Human-Robot Handovers (2015, background): “, [11]) for effective, efficient prediction of user states.”