3 research outputs found
Learning motor skills from partially observed movements executed at different speeds
Learning motor skills from multiple demonstrations
presents a number of challenges. One of those challenges
is the occurrence of occlusions and lack of sensor coverage,
which may corrupt part of the recorded data. Another issue
is the variability in speed of execution of the demonstrations,
which may require a way of finding the correspondence between
the time steps of the different demonstrations. In this paper,
an approach to learn motor skills is proposed that accounts
both for spatial and temporal variability of movements. This
approach, based on an Expectation-Maximization algorithm to
learn Probabilistic Movement Primitives, also allows for learning
motor skills from partially observed demonstrations, which may
result from occlusion or lack of sensor coverage. An application
of the algorithm proposed in this work lies in the field of
Human-Robot Interaction when the robot has to react to human
movements executed at different speeds. Experiments in which
a robotic arm receives a cup handed over by a human illustrate
this application. The capabilities of the algorithm in learning
and predicting movements are also evaluated in experiments
using a data set of letters and a data set of golf putting
movements
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Robots need to be able to adapt to unexpected changes in the environment such
that they can autonomously succeed in their tasks. However, hand-designing
feedback models for adaptation is tedious, if at all possible, making
data-driven methods a promising alternative. In this paper we introduce a full
framework for learning feedback models for reactive motion planning. Our
pipeline starts by segmenting demonstrations of a complete task into motion
primitives via a semi-automated segmentation algorithm. Then, given additional
demonstrations of successful adaptation behaviors, we learn initial feedback
models through learning from demonstrations. In the final phase, a
sample-efficient reinforcement learning algorithm fine-tunes these feedback
models for novel task settings through few real system interactions. We
evaluate our approach on a real anthropomorphic robot in learning a tactile
feedback task.Comment: Submitted to the International Journal of Robotics Research. Paper
length is 21 pages (including references) with 12 figures. A video overview
of the reinforcement learning experiment on the real robot can be seen at
https://www.youtube.com/watch?v=WDq1rcupVM0. arXiv admin note: text overlap
with arXiv:1710.0855
Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives
International audienceThis paper describes our open-source software for predicting the intention of a user physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the intention of the human partner during collaboration, by predicting the future intended trajectory: this capability is critical to design anticipatory behaviors that are crucial in human-robot collaborative scenarios, such as in co-manipulation, cooperative assembly or transportation. We propose an approach to endow the iCub with basic capabilities of intention recognition, based on Probabilistic Movement Primitives (ProMPs), a versatile method for representing, generalizing, and reproducing complex motor skills. The robot learns a set of motion primitives from several demonstrations, provided by the human via physical interaction. During training, we model the collaborative scenario using human demonstrations. During the reproduction of the collaborative task, we use the acquired knowledge to recognize the intention of the human partner. Using a few early observations of the state of the robot, we can not only infer the intention of the partner, but also complete the movement, even if the user breaks the physical interaction with the robot. We evaluate our approach in simulation and on the real iCub. In simulation, the iCub is driven by the user using the Geomagic Touch haptic device. In the real robot experiment, we directly interact with the iCub by grabbing and manually guiding the robot's arm. We realize two experiments on the real robot: one with simple reaching trajectories, and one inspired by collaborative object sorting. The software implementing our approach is open-source and available on the GitHub platform. Additionally, we provide tutorials and videos