Dextrous manipulation from a grasping pose

By C. K. Liu. 2009

137 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Human-Computer Interaction

How researchers see this paper

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

In Control and Systems Engineering

1. Sergey Levine
2. Vikash Kumar
3. Abhishek Gupta
4. Aravind Rajeswaran
5. Pieter Abbeel
6. Chelsea Finn
7. E. Todorov
8. John Schulman

In Human-Computer Interaction

1. E. Todorov
2. Sergey Levine
3. Aravind Rajeswaran
4. Vikash Kumar
5. Abhishek Gupta
6. Jingru Luo
7. Kris K. Hauser
8. John Schulman

Citations in the corpus

• Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (2017, methodology): “. Model-based methods [24] [17] using trajectory optimization and model predictive control have been used to synthesize complex behaviors with high dimensional hands. Sampling [23] and motion capture [22] based approaches have also shown similar success. However, these methods often require detailed”
• Robust trajectory optimization under frictional contact with iterative learning (2015, methodology): “Some numerical approaches have been presented for dexterous manipulation (Liu 2009; Mordatch et al. 2012) as well as non-prehensile grasp (Lertkultanon and Pham 2014; Lynch and Mason 1996; Pham et al. 2014), but extending them to high dimensional problems has proven computationally challenging.”