Making planned paths look more human-like in humanoid robot manipulation planning

By Franziska Zacharias, Christian Schlette, Florian Schmidt, C. Borst, J. Roßmann, G. Hirzinger. 2011

51 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Reinforcement Learning

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

1. Ashutosh Saxena
2. Ashesh Jain

In Control and Systems Engineering

1. Jim Mainprice
2. Dmitry Berenson
3. Ashesh Jain
4. Thorsten Joachims
5. Rachid Alami
6. Ashutosh Saxena
7. Thierry Siméon
8. Shikhar Sharma

In Reinforcement Learning

1. Thorsten Joachims
2. Ashutosh Saxena
3. Ashesh Jain
4. Brian Wojcik
5. Shikhar Sharma
6. Thierry Siméon
7. Sergey Levine
8. Chelsea Finn

Citations in the corpus

• Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “While a robot can reach the same operational space configuration for its wrist with different configurations of the arm, not all of them are preferred [38].”
• Human-robot collaborative manipulation planning using early prediction of human motion (2013, background): “Recent work has explored computing more human-like motions [13] using a reachability map representation.”
• Learning preferences for manipulation tasks from online coactive feedback (2015, background): “They comprise the following three types of the features: 1) Robot Arm Configurations: While a robot can reach the same operational space configuration for its wrist with different configurations of the arm, not all of them are preferred [61].”
• Sharing effort in planning human-robot handover tasks (2012, background): “Other work aim at making humanoid robots motions more human-like [1], [17], [21], [22] to increase the robot-motion legibility.”