Extracting low-dimensional control variables for movement primitives

Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard

doi:10.1109/icra.2015.7139390

Cited by 36 publications

(32 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A relevant example is the probabilistic movement primitives approach proposed by Paraschos et al [72]. The structure of the trajectory distribution defined in [72] requires multiple trajectory demonstrations to avoid overfitting, but the problem can be circumvented by employing factorization and variational inference techniques [77].…”

Section: Gmm With Dynamic Features (Trajectory-gmm)mentioning

confidence: 99%

A tutorial on task-parameterized movement learning and retrieval

Calinon

2015

Intel Serv Robotics

336

348

View full text Add to dashboard Cite

Task-parameterized models of movements aim at automatically adapting movements to new situations encountered by a robot. The task parameters can, for example, take the form of positions of objects in the environment or landmark points that the robot should pass through. This tutorial aims at reviewing existing approaches for task-adaptive motion encoding. It then narrows down the scope to the special case of task parameters that take the form of frames of reference, coordinate systems or basis functions, which are most commonly encountered in service robotics. Each section of the paper is accompanied by source codes designed as simple didactic examples implemented in Matlab with a full compatibility with GNU Octave, closely following the notation and equations of the article. It also presents ongoing work and further challenges that remain to be addressed, with examples provided in simulation and on a real robot (transfer of manipulation behaviors to the Baxter bimanual robot). The repository for the accompanying source codes is available at

show abstract

Section: Gmm With Dynamic Features (Trajectory-gmm)mentioning

confidence: 99%

A tutorial on task-parameterized movement learning and retrieval

Calinon

2015

Intel Serv Robotics

336

348

View full text Add to dashboard Cite

show abstract

“…which is assumed to be approximated well by a Gaussian, or a Gaussian mixture [23], [24]. Thus, ProMPs offer a compact representation of the trajectory distribution in task space, that is, the mean movement, the correlation between the task's variables, and their variance.…”

Section: A Encoding Task Accuracy From Demonstrationsmentioning

confidence: 99%

Probabilistic Prioritization of Movement Primitives

Paraschos

Lioutikov

Peters

et al. 2017

IEEE Robot. Autom. Lett.

Self Cite

234

410

View full text Add to dashboard Cite

Abstract-Movement prioritization is a common approach to combine controllers of different tasks for redundant robots, where each task is assigned a priority. The priorities of the tasks are often hand-tuned or the result of an optimization, but seldomly learned from data. This paper combines Bayesian task prioritization with probabilistic movement primitives to prioritize full motion sequences that are learned from demonstrations. Probabilistic movement primitives (ProMPs) can encode distributions of movements over full motion sequences and provide control laws to exactly follow these distributions. The probabilistic formulation allows for a natural application of Bayesian task prioritization. We extend the ProMP controllers with an additional feedback component that accounts inaccuracies in following the distribution and allows for a more robust prioritization of primitives. We demonstrate how the task priorities can be obtained from imitation learning and how different primitives can be combined to solve even unseen task-combinations. Due to the prioritization, our approach can efficiently learn a combination of tasks without requiring individual models per task combination. Further, our approach can adapt an existing primitive library by prioritizing additional controllers, for example, for implementing obstacle avoidance. Hence, the need of retraining the whole library is avoided in many cases. We evaluate our approach on reaching movements under constraints with redundant simulated planar robots and two physical robot platforms, the humanoid robot "iCub" and a KUKA LWR robot arm.

show abstract

“…The parameters µ w and Σ w can be obtained by providing several demonstrations of the same movement. For each demonstration, we learn the weights w via linear regression or expectation maximization [20], [21]. Subsequently, we estimate the mean µ w and the covariance Σ w via maximum likelihood estimation.…”

Section: A Movement Trajectory Representation As Prompsmentioning

confidence: 99%

Experience Reuse with Probabilistic Movement Primitives

Stark

Peters

Rueckert

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task. We present a framework which is able of such knowledge transfer from already learned movement skills to a new learning task. The framework combines probabilistic movement primitives with descriptions of their effects for skill representation. New skills are first initialized with parameters inferred from related movement primitives and thereafter adapted to the new task through relative entropy policy search. We compare two different transfer approaches to initialize the search space distribution with data of known skills with a similar effect. We show the different benefits of the two knowledge transfer approaches on an object pushing task for a simulated 3-DOF robot. We can show that the quality of the learned skills improves and the required iterations to learn a new task can be reduced by more than 60% when past experiences are utilized.

show abstract

Extracting low-dimensional control variables for movement primitives

Cited by 36 publications

References 17 publications

A tutorial on task-parameterized movement learning and retrieval

A tutorial on task-parameterized movement learning and retrieval

Probabilistic Prioritization of Movement Primitives

Experience Reuse with Probabilistic Movement Primitives

Contact Info

Product

Resources

About