Delivery by hand between human and robot based on fingertip force-torque information

By K. Nagata, Y. Oosaki, M. Kakikura, H. Tsukune. 1998

Subfields: 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 Reinforcement Learning

1. Anca Dragan
2. Siddhartha S. Srinivasa
3. David Hsu
4. Wee Sun Lee
5. Andras Gabor Kupcsik
6. Maya Cakmak
7. Sergey Levine
8. Dorsa Sadigh

In Robotics & Control

1. Anca Dragan
2. Siddhartha S. Srinivasa
3. Jodi L. Forlizzi
4. David Hsu
5. Andras Gabor Kupcsik
6. Maya Cakmak
7. Dmitry Berenson
8. Sergey Levine

Citations in the corpus

• Learning Dynamic Robot-to-Human Object Handover from Human Feedback (2016, background): “Early work on handover dates back to at least 1990s [1, 21].”
• Toward seamless human-robot handovers (2013, background): “Nagata, Oosaki, Kakikura, and Tsukune (1998) presented a grasping system based on force and torque feedback that senses when the humans has a stable grasp on the object, after which the robot can release the object.”
• A human-inspired object handover controller (2013, background): “Nagata et al. (1998) developed a grasp algorithm for a fourfingered gripper that would release the object once the grasp becomes unstable.”
• Human preferences for robot-human hand-over configurations (2011, background): “studied with this approach, including motion control [1], [8], [13], grasp planning [12], [10] and grip forces to be applied during hand-over [15], [9].”