Robot motor skill coordination with EM-based Reinforcement Learning
Petar Kormushev,Sylvain Calinon,Darwin G. Caldwell +2 more
- pp 3232-3237
Reads0
Chats0
TLDR
An approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables through Expectation-Maximization based Reinforcement Learning is presented.Abstract:
We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.read more
Citations
More filters
Book
A Survey on Policy Search for Robotics
TL;DR: This work classifies model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and presents a unified view on existing algorithms.
Journal ArticleDOI
Learning human activities and object affordances from RGB-D videos
TL;DR: In this paper, a structural support vector machine (SSVM) was used to extract a descriptive labeling of the sequence of sub-activities being performed by a human, and more importantly, their interactions with the objects in the form of associated affordances.
Journal ArticleDOI
Policy search for motor primitives in robotics
Jens Kober,Jan Peters +1 more
TL;DR: A novel EM-inspired algorithm for policy learning that is particularly well-suited for dynamical system motor primitives is introduced and applied in the context of motor learning and can learn a complex Ball-in-a-Cup task on a real Barrett WAM™ robot arm.
Proceedings Article
Probabilistic Movement Primitives
TL;DR: This work analytically derive a stochastic feedback controller which reproduces the given trajectory distribution for robot movement control and presents a probabilistic formulation of the MP concept that maintains a distribution over trajectories.
Journal ArticleDOI
The Role of Variability in Motor Learning.
TL;DR: Studies that explore the relationship between motor variability and motor learning in both humans and animal models are reviewed and neural circuit mechanisms that underlie the generation and regulation of motor variability are discussed.
References
More filters
Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
Journal ArticleDOI
The coordination of arm movements: an experimentally confirmed mathematical model.
Tamar Flash,Neville Hogan +1 more
TL;DR: A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements, and is successful only when formulated in terms of the motion of the hand in extracorporal space.
Journal ArticleDOI
A survey of robot learning from demonstration
TL;DR: A comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings, which analyzes and categorizes the multiple ways in which examples are gathered, as well as the various techniques for policy derivation.
Journal ArticleDOI
Optimal feedback control as a theory of motor coordination.
TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.
Book
Robot Programming by Demonstration
TL;DR: Programming by demonstration (PbD) as discussed by the authors is a technique for teaching new skills to a robot by imitation, tutelage, or apprenticeship learning through human guidance.