Towards human-level bimanual dexterous manipulation with reinforcement learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of
dexterous hand manipulation even at the baby level are challenging to solve through …
dexterous hand manipulation even at the baby level are challenging to solve through …
Bi-dexhands: Towards human-level bimanual dexterous manipulation
Achieving human-level dexterity in robotics remains a critical open problem. Even simple
dexterous manipulation tasks pose significant difficulties due to the high number of degrees …
dexterous manipulation tasks pose significant difficulties due to the high number of degrees …
Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills,
making them an appealing component for general-purpose robotic manipulators. However …
making them an appealing component for general-purpose robotic manipulators. However …
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Dexterous multi-fingered hands are extremely versatile and provide a generic way to
perform a multitude of tasks in human-centric environments. However, effectively controlling …
perform a multitude of tasks in human-centric environments. However, effectively controlling …
Deft: Dexterous fine-tuning for real-world hand policies
Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to
perform a host of skills with their hands, from making food to operating tools. In this paper …
perform a host of skills with their hands, from making food to operating tools. In this paper …
Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
SP Arunachalam, S Silwal, B Evans… - 2023 ieee international …, 2023 - ieeexplore.ieee.org
Optimizing behaviors for dexterous manipulation has been a longstanding challenge in
robotics, with a variety of methods from model-based control to model-free reinforcement …
robotics, with a variety of methods from model-based control to model-free reinforcement …
Physics-based dexterous manipulations with estimated hand poses and residual reinforcement learning
G Garcia-Hernando, E Johns… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
Dexterous manipulation of objects in virtual environments with our bare hands, by using only
a depth sensor and a state-of-the-art 3D hand pose estimator (HPE), is challenging. While …
a depth sensor and a state-of-the-art 3D hand pose estimator (HPE), is challenging. While …
H-InDex: Visual reinforcement learning with hand-informed representations for dexterous manipulation
Human hands possess remarkable dexterity and have long served as a source of inspiration
for robotic manipulation. In this work, we propose a human $\textbf {H} $ and-$\textbf {In} …
for robotic manipulation. In this work, we propose a human $\textbf {H} $ and-$\textbf {In} …
Deep imitation learning for bimanual robotic manipulation
F Xie, A Chowdhury… - Advances in neural …, 2020 - proceedings.neurips.cc
We present a deep imitation learning framework for robotic bimanual manipulation in a
continuous state-action space. A core challenge is to generalize the manipulation skills to …
continuous state-action space. A core challenge is to generalize the manipulation skills to …
Dexart: Benchmarking generalizable dexterous manipulation with articulated objects
To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …
objects as humans do. Current robot manipulation has heavily relied on using a parallel …