The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computati...
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ISBN:
(纸本)9781728196817
The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed.
The dynamic and accurate prediction of the target trajectory about UAVs is the key point of airspace management. For the task of air target trajectory prediction dominated by a variety of UAVs, traditional methods can...
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This paper presents a preliminary study on the development of dynamic rolling locomotion for Variable Topology Truss (VTT) robots. Rolling locomotion allows the robot to move in any direction, which is advantageous fo...
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This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our pr...
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ISBN:
(纸本)9781728196817
This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9mm to 4:6mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods.
Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task ef...
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The ability to predict future states is crucial to informed decision-making while interacting with dynamic environments. With cameras providing a prevalent and information-rich sensing modality, the problem of predict...
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ISBN:
(纸本)9798350358513;9798350358520
The ability to predict future states is crucial to informed decision-making while interacting with dynamic environments. With cameras providing a prevalent and information-rich sensing modality, the problem of predicting future states from image sequences has garnered a lot of attention. Current state-of-the-art methods typically train large parametric models for their predictions. Though often able to predict with accuracy these models often fail to provide interpretable confidence metrics around their predictions. Additionally these methods are reliant on the availability of large training datasets to converge to useful solutions. In this paper, we focus on the problem of predicting future images of an image sequence with interpretable confidence bounds from very little training data. To approach this problem, we use non-parametric models to take a probabilistic approach to image prediction. We generate probability distributions over sequentially predicted images, and propagate uncertainty through time to generate a confidence metric for our predictions. Gaussian Processes are used for their data efficiency and ability to readily incorporate new training data online. Our method's predictions are evaluated on a smooth fluid simulation environment. We showcase the capabilities of our approach on real world data by predicting pedestrian flows and weather patterns from satellite imagery.
The rapid growth of electric vehicles (EVs) has led to an increasing demand for charging infrastructure, particularly in off-grid locations. Developing off-grid EV charging stations powered by photovoltaic (PV) system...
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The integration of artificial intelligence (AI) into industrial robotics has revolutionized manufacturing by improving various aspects such as precision, speed, and energy efficiency. This paper delves into AI-driven ...
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This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation b...
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ISBN:
(纸本)9781728196817
This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorporates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing models over the conventional approach that assumes one-to-one relations.
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expect...
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ISBN:
(纸本)9781728196817
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Therefore, generalization capability to unseen domains is crucial for prediction models when autonomous vehicles are deployed in the real world. In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed. We construct a structural causal model for vehicle intention prediction tasks to learn an invariant representation of input driving data for domain generalization. We further integrate a recurrent latent variable model into our structural causal model to better capture temporal latent dependencies from time-series input data. The effectiveness of our approach is evaluated via real-world driving data. We demonstrate that our proposed method has consistent improvement on prediction accuracy compared to other state-of-the-art domain generalization and behavior prediction methods.
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