511 research outputs found
Bootstrapping movement primitives from complex trajectories
Lemme A. Bootstrapping movement primitives from complex trajectories. Bielefeld: Bielefeld University; 2014
Behaviour-driven motion synthesis
Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as
well as to hazardous environments, has led to an increased interest in real-world deployment of
robotic agents. Targeted applications require robots to be adept at synthesising complex
motions rapidly across a wide range of tasks and environments. To this end, this thesis
proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We
formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and
integrate them tightly into the motion synthesis process to make real-world deployment in
complex environments practical. We investigate three principal challenges of this proposition.
Firstly, we argue that behavioural samples in form of trajectories are of particular interest to
guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation
that enables the storage and bootstrap of sets of problem-relevant trajectories.
Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task
instantiations that differ significantly from those stored in the framework. We present two
novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online
modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration
based on trajectory segmentation and malleability.
Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We
propose a multi-layer planner that first solves a simplified version of the problem at hand, to
then inform the search for a solution in the constrained space.
Our contributions on efficient motion synthesis via behaviour guidance augment the robots’
capabilities to deal with more complex planning problems, and do so more effectively than
related approaches in the literature by computing better quality paths in lower response time.
We demonstrate our contributions, in both laboratory experiments and field trials, on a
spectrum of planning problems and robotic platforms ranging from high-dimensional
humanoids and robotic arms with a focus on autonomous manipulation in resembling
environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion
synthesis, we have explored the applicability of our findings on disparate robotic fields, such as
grasp and task planning. We have made some of our contributions open-source hoping they
will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science
Alliance (SICSA
Incremental motor skill learning and generalization from human dynamic reactions based on dynamic movement primitives and fuzzy logic system
Different from previous work on single skill learning from human demonstrations, an incremental motor skill learning, generalization and control method based on dynamic movement primitives (DMP) and broad learning system (BLS) is proposed for extracting both ordinary skills and instant reactive skills from demonstrations, the latter of which is usually generated to avoid a sudden danger (e.g., touching a hot cup). The method is completed in three steps. First, ordinary skills are basically learned from demonstrations in normal cases by using DMP. Then the incremental learning idea of BLS is combined with DMP to achieve multi-stylistic reactive skill learning such that the forcing function of the ordinary skills will be reasonably extended into multiple stylistic functions by adding enhancement terms and updating weights of the radial basis function (RBF) kernels. Finally, electromyography (EMG) signals are collected from human muscles and processed to achieve stiffness factors. By using fuzzy logic system (FLS), the two kinds of skills learned are integrated and generalized in new cases such that not only start, end and scaling factors but also the environmental conditions, robot reactive strategies and impedance control factors will be generalized to lead to various reactions. To verify the effectiveness of the proposed method, an obstacle avoidance experiment that enables robots to approach destinations flexibly in various situations with barriers will be undertaken
Apprenticeship Bootstrapping for Autonomous Aerial Shepherding of Ground Swarm
Aerial shepherding of ground vehicles (ASGV) musters a group of uncrewed ground vehicles (UGVs) from the air using uncrewed aerial vehicles (UAVs). This inspiration enables robust uncrewed ground-air coordination where one or multiple UAVs effectively drive a group of UGVs towards a goal. Developing artificial intelligence (AI) agents for ASGV is a non-trivial task due to the sub-tasks, multiple skills, and their non-linear interaction required to synthesise a solution. One approach to developing AI agents is Imitation learning (IL), where humans demonstrate the task to the machine. However, gathering human data from complex tasks in human-swarm interaction (HSI) requires the human to perform the entire job, which could lead to unexpected errors caused by a lack of control skills and human workload due to the length and complexity of ASGV.
We hypothesise that we can bootstrap the overall task by collecting human data from simpler sub-tasks to limit errors and workload for humans. Therefore, this thesis attempts to answer the primary research question of how to design IL algorithms for multiple agents. We propose a new learning scheme called Apprenticeship Bootstrapping (AB). In AB, the low-level behaviours of the shepherding agents are trained from human data using our proposed hierarchical IL algorithms. The high-level behaviours are then formed using a proposed gesture demonstration framework to collect human data from synthesising more complex controllers. The transferring mechanism is performed by aggregating the proposed IL algorithms.
Experiments are designed using a mixed environment, where the UAV flies in a simulated robotic Gazebo environment, while the UGVs are physical vehicles in a natural environment. A system is designed to allow switching between humans controlling the UAVs using low-level actions and humans controlling the UAVs using high-level actions. The former enables data collection for developing autonomous agents for sub-tasks. At the same time, in the latter, humans control the UAV by issuing commands that call the autonomous agents for the sub-tasks. We baseline the learnt agents against Str\"{o}mbom scripted behaviours and show that the system can successfully generate autonomous behaviours for ASGV
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