242 research outputs found
Adaptive modular architectures for rich motor skills: technical report on the cognitive architecture
A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks
Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons
The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller
Proposed methods for efficiently attaining the skills required for accurate time keeping in music
This dissertation aims to provide exercises that will efficiently develop the skills necessary for accurate time keeping in music. Many contemporary industry-standard texts fail to provide specific methods to develop accurate time keeping in music. Most include instructions to use a metronome, but only as a dictator of tempo and not as a tool to improve one\u27s own time keeping ability. This dissertation proposes to address that gap in contemporary music instructional literature. To enhance the understanding of the skills required for accurate time keeping in music, contemporary neurological literature pertaining to time keeping is investigated. A review of this literature reveals that temporal cognition and motor learning are two main factors influencing time keeping. Based on these findings, forty-eight exercises are developed that are designed to optimally input the cognitive and motor skills required to achieve the physical manifestation of accurate metronomic temporal intervals. The exercises focus primarily on heightening the awareness of the temporal partials (known in music as subdivisions) that are not being physically output by the individual, so that they may accurately quantify, perceive and physically manifest metronomically accurate notes or strokes. Through analysis of selected industry-standard music instructional literature, a format of presentation for the exercises is developed and employed. The devised format comprises of an introduction to the text, explanation of exercises, presentation of exercises in both written text and musical notation, and aural demonstrations of the exercises on an included compact disc. The exercises are then summarised and suggestions of extrapolations for exercises are discussed
IntelligentAutonomous SystemsLearningSequential SkillsforRobot Manipulation Tasks
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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
Development of PVDF tactile dynamic sensing in a behaviour-based assembly robot
The research presented in this thesis focuses on the development of tactile event sig¬
nature sensors and their application, especially in reactive behaviour-based robotic
assembly systems.In pursuit of practical and economic sensors for detecting part contact, the application
ofPVDF (polyvinylidene fluoride) film, a mechanical vibration sensitive piezo material,
is investigated. A Clunk Sensor is developed which remotely detects impact vibrations,
and a Push Sensor is developed which senses small changes in the deformation of a
compliant finger surface. The Push Sensor is further developed to provide some force
direction and force pattern sensing capability.By being able to detect changes of state in an assembly, such as a change of contact
force, an assembly robot can be well informed of current conditions. The complex
structure of assembly tasks provides a rich context within which to interpret changes
of state, so simple binary sensors can conveniently supply a lot more information than
in the domain of mobile robots. Guarded motions, for example, which require sensing a
change of state, have long been recognised as very useful in part mating tasks. Guarded
motions are particularly well suited to be components of assembly behavioural modules.In behaviour-based robotic assembly systems, the high level planner is endowed with
as little complexity as possible while the low level planning execution agent deals with
actual sensing and action. Highly reactive execution agents can provide advantages by
encapsulating low level sensing and action, hiding the details of sensori-motor complexity from the higher levels.Because behaviour-based assembly systems emphasise the utility of this kind of quali¬
tative state-change sensor (as opposed to sensors which measure physical quantities),
the robustness and utility of the Push Sensor was tested in an experimental behaviourbased system. An experimental task of pushing a ring along a convoluted stiff wire is
chosen, in which the tactile sensors developed here are aided by vision. Three differ¬
ent methods of combining these different sensors within the general behaviour-based
paradigm are implemented and compared. This exercise confirms the robustness and
utility of the PVDF-based tactile sensors. We argue that the comparison suggests
that for behaviour-based assembly systems using multiple concurrent sensor systems,
bottom-level motor control in terms of force or velocity would be more appropriate
than positional control. Behaviour-based systems have traditionally tried to avoid
symbolic knowledge. Considering this in the light of the above work, it was found
useful to develop a taxonomy of type of knowledge and refine the prohibition
Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs
Abstract—For robots, the ability to model human configura-tions and temporal dynamics is crucial for the task of anticipating future human activities, yet requires conflicting properties: On one hand, we need a detailed high-dimensional description of human configurations to reason about the physical plausibility of the prediction; on the other hand, we need a compact representation to be able to parsimoniously model the relations between the human and the environment. We therefore propose a new model, GP-LCRF, which admits both the high-dimensional and low-dimensional representation of humans. It assumes that the high-dimensional representation is generated from a latent variable corresponding to its low-dimensional representation using a Gaussian process. The gener-ative process not only defines the mapping function between the high- and low-dimensional spaces, but also models a distribution of humans embedded as a potential function in GP-LCRF along with other potentials to jointly model the rich context among humans, objects and the activity. Through extensive experiments on activity anticipation, we show that our GP-LCRF consistently outperforms the state-of-the-art results and reduces the predicted human trajectory error by 11.6%. I
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