Exploiting kinematic redundancy for robotic grasping of multiple objects
Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously
perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi …
perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi …
Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network
Human hands can perform complex manipulation of various objects. It is beneficial if
anthropomorphic robotic hands can manipulate objects like human hands. However, it is still …
anthropomorphic robotic hands can manipulate objects like human hands. However, it is still …
Soft-grasping with an anthropomorphic robotic hand using spiking neurons
Evolution gave humans advanced grasping capabilities combining an adaptive hand with
efficient control. Grasping motions can quickly be adapted if the object moves or deforms …
efficient control. Grasping motions can quickly be adapted if the object moves or deforms …
Riemannian geometry as a unifying theory for robot motion learning and control
Riemannian geometry is a mathematical field which has been the cornerstone of
revolutionary scientific discoveries such as the theory of general relativity. Despite early …
revolutionary scientific discoveries such as the theory of general relativity. Despite early …
Reach to grasp planning for a synergy-controlled robotic hand based on pesudo-distance formulation
In the past several years, grasp analysis of multi-fingered robotic hands has been actively
studied through the use of posture synergies. In these grasping planning algorithms, a …
studied through the use of posture synergies. In these grasping planning algorithms, a …
[HTML][HTML] Robotic hand synergies for in-hand regrasping driven by object information
We develop a conditional generative model to represent dexterous grasp postures of a
robotic hand and use it to generate in-hand regrasp trajectories. Our model learns to encode …
robotic hand and use it to generate in-hand regrasp trajectories. Our model learns to encode …
Learning conditional postural synergies for dexterous hands: A generative approach based on variational auto-encoders and conditioned on object size and category
D Dimou, J Santos-Victor… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Postural synergies are used in robotics to facilitate the control of dexterous artificial hands.
This is achieved by learning a latent space (synergy space) from grasp postures and directly …
This is achieved by learning a latent space (synergy space) from grasp postures and directly …
[HTML][HTML] Learning grasp configuration through object-specific hand primitives for posture planning of anthropomorphic hands
B Liu, L Jiang, S Fan, J Dai - Frontiers in neurorobotics, 2021 - frontiersin.org
The proposal of postural synergy theory has provided a new approach to solve the problem
of controlling anthropomorphic hands with multiple degrees of freedom. However …
of controlling anthropomorphic hands with multiple degrees of freedom. However …
Biomorphic robot controls: event driven model free deep SNNs for complex visuomotor tasks
R Dillmann, A Rönnau - Artificial Life and Robotics, 2022 - Springer
The human brain surpass conventional computer architectures in regard to energy
efficiency, robustness, connectivity and adaptivity. These aspects are inspiring today's …
efficiency, robustness, connectivity and adaptivity. These aspects are inspiring today's …
Reducing anthropomorphic hand degrees of actuation with grasp-function-dependent and joint-element-sparse hand synergies
B Liu, L Jiang, S Fan - International Journal of Humanoid Robotics, 2021 - World Scientific
In this paper, a set of grasp-function-dependent and joint-element-sparse hand synergies
was proposed. First, hand synergies were extracted from five basic categories of movements …
was proposed. First, hand synergies were extracted from five basic categories of movements …