On dexterity and dexterous manipulation

By Raymond R. Ma, Aaron M. Dollar. 2011

172 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Control and Systems Engineering

How researchers see this paper

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

In Cognitive Neuroscience

1. Gerhard Neumann
2. Jan Peters
3. Lilian Weng
4. Marcin Andrychowicz
5. Wojciech Zaremba
6. Herke Van Hoof
7. OpenAI
8. Sergey Levine

In Control and Systems Engineering

1. Sergey Levine
2. Pieter Abbeel
3. Gerhard Neumann
4. Jan Peters
5. Wojciech Zaremba
6. Marcin Andrychowicz
7. Herke Van Hoof
8. Chelsea Finn

Citations in the corpus

• Solving Rubik's Cube with a Robot Hand (2019, background): “Dexterous manipulation has been an active area of research for decades [32, 91, 9, 74, 64].”
• Learning dexterous in-hand manipulation (2018, background): “Furthermore, the policy also naturally discovers many strategies for dexterous in-hand manipulation described by the robotics community [37] such as”
• Learning robot in-hand manipulation with tactile features (2015, background): “Objects cannot always be picked up in these configurations; however, in-hand manipulation enables a robot to reconfigure an object [1].”
• Dexterous manipulation using both palm and fingers (2014, background): “Ma and Dollar [1] argued that a simple gripper and a dexterous arm is sufficient for many applications, but a dexterous end-effector can increase the workspace of the arm.”
• Learning Dexterous In-Hand Manipulation. (2018, background): “Furthermore, the policy also naturally discovers many strategies for dexterous in-hand manipulation described by the robotics community [37] such as”