Synthesizing robotic handwriting motion by learning from human demonstrations

This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the robot to learn themselves the handwriting skill. The framework adapts the principle behind ensemble methods - where improved performance is obtained by combining the output of multiple simple algorithms - in an inverse optimal control problem. This integration addresses the challenges of rapid extraction and representation of multiple-mode motion trajectories, with the cost forms which are transferable and interpretable in the development of the robot compliance control. It also introduces the incorporation of a human movement inspired feature, which provides intuitive motion modulation to generalize the synthesis with poor robotic written samples for children to identify and correct. We present the results on the success of synthesizing a variety of natural-looking motion samples based upon the learned cost functions. The framework is validated by a user study, where the synthesized dynamical motion is shown to be hard to distinguish from the real human handwriting.info:eu-repo/semantics/publishedVersio

Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations

This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the robot to learn themselves the handwriting skill. The framework adapts the principle behind ensemble methods - where improved performance is obtained by combining the output of multiple simple algorithms - in an inverse optimal control problem. This integration addresses the challenges of rapid extraction and representation of multiple-mode motion trajectories, with the cost forms which are transferable and interpretable in the development of the robot compliance control. It also introduces the incorporation of a human movement inspired feature, which provides intuitive motion modulation to generalize the synthesis with poor robotic written samples for children to identify and correct. We present the results on the success of synthesizing a variety of natural-looking motion samples based upon the learned cost functions. The framework is validated by a user study, where the synthesized dynamical motion is shown to be hard to distinguish from the real human handwriting

Incorporating Human Expertise in Robot Motion Learning and Synthesis

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots need to be programmed for such types of complex scenarios, which demands significant domain knowledge in robotics and control. One viable approach to enable robots to acquire skills in a more flexible and efficient way is by giving them the capabilities of autonomously learn from human demonstrations and expertise through interaction. Such framework helps to make the creation of skills in robots more social and less demanding on programing and robotics expertise. Yet, current imitation learning approaches suffer from significant limitations, mainly about the flexibility and efficiency for representing, learning and reasoning about motor tasks. This thesis addresses this problem by exploring cost-function-based approaches to learning robot motion control, perception and the interplay between them. To begin with, the thesis proposes an efficient probabilistic algorithm to learn an impedance controller to accommodate motion contacts. The learning algorithm is able to incorporate important domain constraints, e.g., about force representation and decomposition, which are nontrivial to handle by standard techniques. Compliant handwriting motions are developed on an articulated robot arm and a multi-fingered hand. This work provides a flexible approach to learn robot motion conforming to both task and domain constraints. Furthermore, the thesis also contributes with techniques to learn from and reason about demonstrations with partial observability. The proposed approach combines inverse optimal control and ensemble methods, yielding a tractable learning of cost functions with latent variables. Two task priors are further incorporated. The first human kinematics prior results in a model which synthesizes rich and believable dynamical handwriting. The latter prior enforces dynamics on the latent variable and facilitates a real-time human intention cognition and an on-line motion adaptation in collaborative robot tasks. Finally, the thesis establishes a link between control and perception modalities. This work offers an analysis that bridges inverse optimal control and deep generative model, as well as a novel algorithm that learns cost features and embeds the modal coupling prior. This work contributes an end-to-end system for synthesizing arm joint motion from letter image pixels. The results highlight its robustness against noisy and out-of-sample sensory inputs. Overall, the proposed approach endows robots the potential to reason about diverse unstructured data, which is nowadays pervasive but hard to process for current imitation learning

Associate Latent Encodings in Learning from Demonstrations

We contribute a learning from demonstration approach for robots to acquire skills from multi-modal high-dimensional data. Both latent representations and associations of different modalities are proposed to be jointly learned through an adapted variational auto-encoder. The implementation and results are demonstrated in a robotic handwriting scenario, where the visual sensory input and the arm joint writing motion are learned and coupled. We show the latent representations successfully construct a task manifold for the observed sensor modalities. Moreover, the learned associations can be exploited to directly synthesize arm joint handwriting motion from an image input in an end-to-end manner. The advantages of learning associative latent encodings are further highlighted with the examples of inferring upon incomplete input images. A comparison with alternative methods demonstrates the superiority of the present approach in these challenging tasks

An ensemble inverse optimal control approach for robotic task learning and adaptation

This paper contributes a novel framework to efficiently learn cost-to-go function representations for robotic tasks with latent modes. The proposed approach relies on the principle behind ensemble methods, where improved performance is obtained by aggregating a group of simple models, each of which can be efficiently learnedq. The maximum-entropy approximation is adopted as an effective initialization and the quality of this surrogate is guaranteed by a theoretical bound. Our approach also provides an alternative perspective to view the popular mixture of Gaussians under the framework of inverse optimal control. We further propose to enforce a dynamics on the model ensemble, using Kalman estimation to infer and modulate model modes. This allows robots to exploit the demonstration redundancy and to adapt to human interventions, especially in tasks where sensory observations are non-Markovian. The framework is demonstrated with a synthetic inverted pendulum example and online adaptation tasks, which include robotic handwriting and mail delivery

Affect of Robot’s Competencies on Children’s Perception

The focus of the research described in this paper is to explore children’s perception of a social robot’s learning abilities and behavior in an educational context. With this purpose, we conducted a long-term study with children in a school by adopting the learning-by-teaching learning method. The scenario involves a ”learner-agent” (a robot) which seeks help from a child (a teacher) in correcting the shapes of a few letters it writes. Two versions of the robot were built: one where it learns and another where it does not improve over time. The results of the study suggest that children’s social relationship with the robot was not affected by the learning abilities of the agent

Developing Learning Scenarios to Foster Children's Handwriting Skills with the Help of Social Robots

Social robots are being used to create better educationalscenarios, boosting children’s motivation and engagement.The focus of the research is to explore new ways to supportchildren in acquisition of their handwriting skills with thehelp of a social robot. With this perspective, three studiesare discussed to investigate aspects related to the learningmodes of child-robot interaction, children’s impression of asocial robot and classification of children’s common hand-writing difficultie

Sim2Real Neural Controllers for Physics-based Robotic Deployment of Deformable Linear Objects

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task -- accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.Comment: YouTube video: https://youtu.be/OSD6dhOgyMA?feature=share

Classification of Children's Handwriting Errors for the Design of an Educational Co-writer Robotic Peer

In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which were then revised in a second study. The second study also analyses the "peer-learning" (PL) and "peer-tutoring" (PT) learning methods in an educational scenario, where a pair of children perform a collaborative writing activity in the presence of a robot facilitator. The data obtained in the first two studies allowed us to create a "taxonomy of handwriting errors". A set of writing errors were selected and implemented in an educational activity for validation. This activity constituted a third study, wherein we systematically induced the errors into a Nao robot's handwriting using the {PT} method - A teacher-child corrects the handwriting errors of the learner-robot. The preliminary results suggest that the children in general showed awareness to the writing errors and were able to perceive the writing abilities of the robot