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A. Agung Julius

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

ICLR Conference 2025 Conference Paper

Shedding Light on Time Series Classification using Interpretability Gated Networks

Yunshi Wen
Tengfei Ma 0001
Ronny Luss
Debarun Bhattacharjya
Achille Fokoue
A. Agung Julius

In time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility. In this work, we present InterpGN, a framework that integrates an interpretable model and a deep neural network. Within this framework, we introduce a novel gating function design based on the confidence of the interpretable expert, preserving interpretability for samples where interpretable features are significant while also identifying samples that require additional expertise. For the interpretable expert, we incorporate shapelets to effectively model shape-level features for time-series data. We introduce a variant of Shapelet Transforms to build logical predicates using shapelets. Our proposed model achieves comparable performance with state-of-the-art deep learning models while additionally providing interpretable classifiers for various benchmark datasets. We further show that our models improve on quantitative shapelet quality and interpretability metrics over existing shapelet-learning formulations. Finally, we show that our models can integrate additional advanced architectures and be applied to real-world tasks beyond standard benchmarks such as the MIMIC-III and time series extrinsic regression datasets.

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ICRA Conference 2023 Conference Paper

High-Speed High-Accuracy Spatial Curve Tracking Using Motion Primitives in Industrial Robots

Honglu He
Chen-Lung Lu
Yunshi Wen
Glenn Saunders
Pinghai Yang
Jeffrey Schoonover
John D. Wason
A. Agung Julius

Industrial robots are increasingly deployed in applications requiring an end effector tool to closely track a specified path, such as in spraying and welding. Performance and productivity present possibly conflicting objectives: tracking accuracy, path speed, and motion uniformity. Industrial robots are programmed through motion primitives consisting of waypoints connected by pre-defined motion segments, with specified parameters such as path speed and blending zone. The actual executed robot motion depends on the robot joint servo controller and joint motion constraints (e. g. , velocity, acceleration limits) which are largely unknown to the users. Programming a robot to achieve the desired performance today is time-consuming and mostly manual, requiring tuning a large number of coupled parameters in the motion primitives. The performance also depends on the choice of additional param-eters: possible redundant degrees of freedom, location of the target curve, and the robot configuration. This paper presents a systematic approach to optimize robot motion parameters. The approach first selects the static parameters, then chooses the motion primitives, and finally iteratively updates the waypoints to minimize the tracking error. The ultimate performance objective is to maximize the path speed subject to the tracking accuracy and speed uniformity constraints over the entire path. We have demonstrated the effectiveness of this approach both in simulation and on physical systems for ABB and FANUC robots applied to two challenging example curves. Comparing with the baseline using the current industry practice, the optimized performance shows over 100% performance improvement.

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ICLR Conference 2023 Conference Paper

Weighted Clock Logic Point Process

Ruixuan Yan
Yunshi Wen
Debarun Bhattacharjya
Ronny Luss
Tengfei Ma 0001
Achille Fokoue
A. Agung Julius

Datasets involving multivariate event streams are prevalent in numerous applications. We present a novel framework for modeling temporal point processes called clock logic neural networks (CLNN) which learn weighted clock logic (wCL) formulas as interpretable temporal rules by which some events promote or inhibit other events. Specifically, CLNN models temporal relations between events using conditional intensity rates informed by a set of wCL formulas, which are more expressive than related prior work. Unlike conventional approaches of searching for generative rules through expensive combinatorial optimization, we design smooth activation functions for components of wCL formulas that enable a continuous relaxation of the discrete search space and efficient learning of wCL formulas using gradient-based methods. Experiments on synthetic datasets manifest our model's ability to recover the ground-truth rules and improve computational efficiency. In addition, experiments on real-world datasets show that our models perform competitively when compared with state-of-the-art models.

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ICRA Conference 2015 Conference Paper

Algorithms for simultaneous motion control of multiple T. pyriformis cells: Model predictive control and Particle Swarm Optimization

Yan Ou
Peter Kang
Min Jun Kim 0001
A. Agung Julius

This paper investigates the use of single control signal (magnetic field direction) and PSO-MPC algorithm to control multiple magnetized Tetrahymena pyriformis (T. pyriformis) cells to move from their initial positions to their target positions simultaneously while avoiding the obstacle. The magnetized T. pyriformis cells are generated by adding iron-oxide spherical particles into the cells. We control the cells' moving direction by changing the magnetic field direction. Based on Model Predictive Control (MPC) algorithm, we define a cost function which is composed of the target cost function and the obstacle potential function. The target cost function is to measure the sum of differences between cells' predicted positions and their target positions. The obstacle potential function is used to measure the repulsive force of the obstacle. The input variables of the cost function are the sequence of control signals. We use Particle Swarm Optimization (PSO) method to find a cost value which is close to the global minimum of the cost function. In the experimental result section, we show the control of three m3pi robots to move from their initial positions to their target positions with avoiding the obstacle. Since the similar control strategy has successfully controlled one T. pyriformis cell in our previous work, we believe our PSO-MPC algorithm is applicable on the multiple T. pyriformis cells' control task.

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IROS Conference 2015 Conference Paper

Dynamic obstacle avoidance for bacteria-powered microrobots

Hoyeon Kim
U. Kei Cheang
A. Agung Julius
Min Jun Kim 0001

As microscale robots are becoming increasingly popular due to their potential for medical and industrial applications, various designs of microscale robotic system have been developed. However, there has not been much work on autonomous control algorithms for microscale robots in microfluidic environments. In this paper, we introduce an autonomous navigation algorithm for the bacteria-powered microrobots (BPMs) in a workspace with moving obstacles. A BPM consists of a rigid inorganic body with bacteria attached on the surface. The attached bacteria provide propulsive force and are controllable using electric fields, which had been demonstrated in previous work. We take the controllability of BPMs and the unpredictable motion of dynamic obstacles into account to develop a dynamic obstacle avoidance approach. Moreover, we use finite element simulation to observe an electric field around a moving obstacle to model the field's deformation. Demonstration of dynamic obstacle avoidance approach through simulation results and experimental data are presented in the paper.

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IROS Conference 2013 Conference Paper

Feedback control of many magnetized: Tetrahymena pyriformis cells by exploiting phase inhomogeneity

Aaron T. Becker
Yan Ou
Paul Seung Soo Kim
Min Jun Kim 0001
A. Agung Julius

Biological robots can be produced in large numbers, but are often controlled by uniform inputs. This makes position control of multiple robots inherently challenging. This paper uses magnetically-steered ciliate eukaryon {Tetrahymena pyriformis) as a case study. These cells swim at a constant speed, and can be turned by changing the orientation of an external magnetic field. We show that it is possible to steer multiple T. pyriformis to independent goals if their turning — modeled as a first-order system — has unique time constants. We provide system identification tools to parameterize multiple cells in parallel. We construct feedback control-Lyapunov methods that exploit differing phase-lags under a rotating magnetic field to steer multiple cells to independent target positions. We prove that these techniques scale to any number of cells with unique first-order responses to the global magnetic field. We provide simulations steering hundreds of cells and validate our procedure in hardware experiments with multiple cells.

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ICRA Conference 2012 Conference Paper

Motion control of Tetrahymena pyriformis cells with artificial magnetotaxis: Model Predictive Control (MPC) approach

Yan Ou
Dal Hyung Kim
Paul Seung Soo Kim
Min Jun Kim 0001
A. Agung Julius

The use of live microbial cells as microscale robots is an attractive premise, primarily because they are easy to produce and to fuel. In this paper, we study the motion control of magnetotactic Tetrahymena pyriformis cells. Magnetotactic T. pyriformis is produced by introducing artificial magnetic dipole into the cells. Subsequently, they can be steered by using an external magnetic field. We observe that the external magnetic field can only be used to affect the swimming direction of the cells, while the swimming velocity depends largely on the cells' own propulsion. Feedback information for control is obtained from a computer vision system that tracks the cell. The contribution of this paper is twofold. First, we construct a discrete-time model for the cell dynamics that is based on first principle. Subsequently, we identify the model parameters using the Least Squares approach. Second, we formulate a model predictive approach for feedback control of magnetotactic T. pyriformis. Both the model fitness and the performance of the feedback controller are verified using experimental data.

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ICRA Conference 2012 Conference Paper

Three-dimensional control of engineered motile cellular microrobots

Dal Hyung Kim
Paul Seung Soo Kim
A. Agung Julius
Min Jun Kim 0001

We demonstrate three-dimensional control with the eukaryotic cell Tetrahymena pyriformis (T. pyriformis) using two sets of Helmholtz coils for xy-plane motion and a single electromagnet for vertical motion. T. pyriformis is modified to have artificial magnetotaxis with internalized magnetite. Since the magnetic fields exerted by electromagnets are relatively uniform in the working space, the magnetite exerts only torque, without translational force, which enabled us to guide the cell's swimming direction while the swimming force is exerted only by the cell's motile organelles. A stronger magnetic force was necessary to steer cells to the z-axis, and, as a result, a single electromagnet placed just below our sample area is utilized for vertical motion. To track the cell's positions in the z-axis, intensity profiles of non-motile cells at varying distances from the focal plane are used. During vertical motion along the z-axis, the intensity difference from the background decreases while the cell size increases. Since the cell is pear-shaped, the eccentricity is high during planar motion, but lowers during vertical motion due to the change in orientation. The three-dimensional control of the live organism T. pyriformis as a cellular robot shows great potential to be utilized for practical applications in microscale tasks, such as target transport and cell therapy.

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ICRA Conference 2011 Conference Paper

Real-time feedback control using artificial magnetotaxis with rapidly-exploring random tree (RRT) for Tetrahymena pyriformis as a microbiorobot

Dal Hyung Kim
Sean Brigandi
A. Agung Julius
Min Jun Kim 0001

In this paper, we present a control strategy using real-time feedback combined with feasible path planning to manipulate a type of microorganism, Tetrahymena pyriformis (T. pyriformis), as a micro-bio-robot using artificial magnetotaxis. Artificially magnetotactic T. pyriformis cells were created by the internalization of iron oxide nano particles. Following the magnetization of the internalized particles, the cells become controllable using an external time-varying magnetic field. The behavior of artificially magnetotactic T. pyriformis under a magnetic field has been investigated in a manual control experiment. A feasible path planner called rapidly-exploring random tree (RRT) and a feedback control scheme are implemented to guide the cell to a desired position and orientation. Since the motion of T. pyriformis is nonlinear like that of a car, combining the RRT and feedback control allows the cell to be controlled in 3-dimensional (x, y, ¸) space. In the results, real-time feedback control of T. pyriformis in 3-dimensional space demonstrated the potential of utilizing T. pyriformis as a micro-bio-robot for microscale tasks.

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ICRA Conference 2010 Conference Paper

Biosensing and actuation for microbiorobots

Mahmut Selman Sakar
Edward B. Steager
A. Agung Julius
Min Jun Kim 0001
Vijay Kumar 0001
George J. Pappas

In this paper, we describe how signaling networks and actuation in bacterial cells and biomolecular networks of bacteria can be used to develop an integrated micro-bio-robotic system. SU8 microstructures blotted with swarmer cells of Serratia Marcescens in a monolayer are propelled by the bacteria in the absence of any environmental stimulus. We call such microstructures with bacteria Micro Bio Robots (MBRs) and the uncontrolled motion in the absence of stimuli self actuation. Our paper has two primary contributions. First, we demonstrate the control of MBRs using self-actuation, DC electric fields and ultra-violet radiation, and develop experimentally validated mathematical model for the MBRs. This model allows us to use self-actuation and electrokinetic actuation to steer the MBR to any position and orientation in a planar micro channel. Second, we describe the development of biosensors for the MBRs. This is done by attaching genetically engineered Escherichia coli cells that are capable of sensing nonmetabolizable lactose analog methyl-β-D-thiogalactoside (TMG). We describe the fabrication process for MBRs and show experimental results demonstrating sensing, actuation and control.

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ICRA Conference 2009 Conference Paper

Harnessing bacterial power in microscale actuation

A. Agung Julius
Mahmut Selman Sakar
Edward B. Steager
U. Kei Cheang
Min Jun Kim 0001
Vijay Kumar 0001
George J. Pappas

This paper presents a systematic analysis of the motion of microscale structures actuated by flagellated bacteria. We perform the study both experimentally and theoretically. We use a blotting procedure to attach flagellated bacteria to a buoyancy-neutral plate called a microbarge. The motion of the plate depends on the distribution of the cells on the plate and the stimuli from the environment. We construct a stochastic mathematical model for the system, based on the assumption that the behavior of each bacterium is random and independent of that of its neighbors. The main finding of the paper is that the motion of the barge plus bacteria system is a function of a very small set of parameters. This reduced-dimensional model can be easily estimated using experimental data. We show that the simulation results obtained from the model show an excellent match with the experimentally-observed motion of the barge.

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