• Corpus ID: 44746799

xtracting Bimanual Synergies with Reinforcement Learning

@inproceedings{Luck2017xtractingBS,
  title={xtracting Bimanual Synergies with Reinforcement Learning},
  author={Kevin Sebastian Luck and Heni Ben Amor},
  year={2017}
}
Motor synergies are an important concept in human motor control. Through the co-activation of multiple muscles, complex motion involving many degrees-of-freedom can be generated. However, leveraging this concept in robotics typically entails using human data that may be incompatible for the kinematics of the robot. In this paper, our goal is to enable a robot to identify synergies for low-dimensional control using trial-and-error only. We discuss how synergies can be learned through latent… 
interactive-robotics.engineering.asu.edu

References

SHOWING 1-10 OF 22 REFERENCES
Extracting motor synergies from random movements for low-dimensional task-space control of musculoskeletal robots
TLDR
This paper proposes to extract motor synergies from a subset of randomly generated reaching-like movement data, and presents a kernel-based regression formulation to estimate the forward and the inverse dynamics, and a sliding controller in order to cope with estimation error.
Kinesthetic Bootstrapping: Teaching Motor Skills to Humanoid Robots through Physical Interaction
TLDR
A new programming-by-demonstration method called Kinesthetic Bootstrapping for teaching motor skills to humanoid robots by means of intuitive physical interactions, which has been successfully applied to the learning of various complex motor skills such as walking and standing up.
Latent space policy search for robotics
TLDR
This paper presents a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies by performing dimensionality reduction as a preprocessing step but naturally combines it with policy search.
Hand Posture Subspaces for Dexterous Robotic Grasping
TLDR
An on-line grasp planner that allows a human operator to perform dexterous grasping tasks using an artificial hand in a hand posture subspace of highly reduced dimensionality is presented.
Subject-specific muscle synergies in human balance control are consistent across different biomechanical contexts.
TLDR
It is shown that trial-by-trial variations in muscle activation for multidirectional balance control in humans were constrained by a small set of muscle synergies, and Muscle synergies represent consistent motor modules that map intention to action, regardless of the biomechanical context of the task.
Sparse Latent Space Policy Search
TLDR
A reinforcement learning method for sample-efficient policy search that exploits correlations between control variables, particularly frequent in motor skill learning tasks, and outperforms state-of-the-art policy search methods.
Goal directed multi-finger manipulation: Control policies and analysis
Identification of muscle synergies associated with gait transition in humans
TLDR
The results suggest that the CNS low-dimensionally regulate the activation profiles of the specific synergies based on afferent information (spontaneous gait Transition) or by changing only the descending neural input to the muscle synergies (voluntary gait transition) to achieve a gait transitions.
Postural Hand Synergies for Tool Use
TLDR
The results suggest that the control of hand posture involves a few postural synergies, regulating the general shape of the hand, coupled with a finer control mechanism providing for small, subtle adjustments.
A novel type of compliant and underactuated robotic hand for dexterous grasping
TLDR
RBO Hand 2 is presented, a highly compliant, underactuated, robust, and dexterous anthropomorphic hand that is inexpensive to manufacture and the morphology can easily be adapted to specific applications, and it is demonstrated that complex grasping behavior can be achieved with relatively simple control.
...
...