Real-time behaviour synthesis for dynamic hand-manipulation
By Vikash Kumar, Yuval Tassa, Tom Erez, E. Todorov. 2014
86 citations recorded by the source corpus.
Subfields: Biomedical Engineering, 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 Biomedical Engineering
- Sergey Levine
- Vikash Kumar
- Aravind Rajeswaran
- Abhishek Gupta
- Pieter Abbeel
- Pulkit Agrawal
- Tao Chen
- Jie Xu
In Control and Systems Engineering
- Sergey Levine
- Vikash Kumar
- Abhishek Gupta
- Pieter Abbeel
- Aravind Rajeswaran
- Chelsea Finn
- Pulkit Agrawal
- E. Todorov
Citations in the corpus
- Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (2017, methodology): “a) Model-based trajectory optimization: : Modelbased trajectory optimization methods [31], [24], [38] have demonstrated impressive results in simulated domains, particularly when the dynamics can be adjusted or relaxed to make them more tractable (as, e.”
- Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost (2018, background): “In simulation, [10], [11], [12] have shown the ability to synthesize complex behaviors when provided with a ground truth model.”
- Physically consistent state estimation and system identification for contacts (2015, methodology): “Using a physics simulator such as MuJoCo bridges the gap towards control as MuJoCo has been successfully applied in trajectory optimization for control [2] [1].”
- A System for General In-Hand Object Re-Orientation (2021, methodology): “Past work has tackled the in-hand reorientation problem via several approaches: (i) The use of analytical models with powerful trajectory optimization methods [1, 2, 3].”
- Manipulators and Manipulation in high dimensional spaces (2016, methodology): “We modelled the Modular Prosthetic Limb (MPL) [66] and ADROIT hand [47] (Fig 9.”
- Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction (2019, methodology): “Most approaches therefore use trajectory optimization to provide a controller [3], [16], [17].”
- MuJoCo HAPTIX: A virtual reality system for hand manipulation (2015, methodology): “We modelled the Modular Prosthetic Limb (MPL) [22] and ADROIT hand [23] (Fig 5) for testing purposes.”