Author name cluster

Malcolm A. MacIver

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.

8 papers

1 author row

ICML Conference 2025 Conference Paper

Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents

Shuo Han
German Espinosa
Junda Huang
Daniel A. Dombeck
Malcolm A. MacIver
Bradly C. Stadie

Recent advances in reinforcement learning (RL) have demonstrated impressive capabilities in complex decision-making tasks. This progress raises a natural question: how do these artificial systems compare to biological agents, which have been shaped by millions of years of evolution? To help answer this question, we undertake a comparative study of biological mice and RL agents in a predator-avoidance maze environment. Through this analysis, we identify a striking disparity: RL agents consistently demonstrate a lack of self-preservation instinct, readily risking “death” for marginal efficiency gains. These risk-taking strategies are in contrast to biological agents, which exhibit sophisticated risk-assessment and avoidance behaviors. Towards bridging this gap between the biological and artificial, we propose two novel mechanisms that encourage more naturalistic risk-avoidance behaviors in RL agents. Our approach leads to the emergence of naturalistic behaviors, including strategic environment assessment, cautious path planning, and predator avoidance patterns that closely mirror those observed in biological systems.

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IROS Conference 2014 Conference Paper

Improving object tracking through distributed exploration of an information map

Izaak D. Neveln
Lauren M. Miller
Malcolm A. MacIver
Todd D. Murphey

Tracking the position of moving objects requires tight coordination of sensing and movement, in both biological contexts such as prey pursuit and capture, and in target localization by mobile robots. Algorithms for target tracking often use a probabilistic map, or information map, of the domain to guide active search. Though it is reasonable to expect that the best approach would be to choose control actions driving the robot toward the maximum of this information map, we show improved performance in simulation by using a simple heuristic incorporating the time history of robot movement into the map. Furthermore, our results indicate that as the distribution of robot positions approaches the distribution of the density of information, the variance of the estimate is decreased and tracking improves. We conclude that control actions based solely on information maximization may under-perform in information orientated tasks, such as the estimation of moving target positions.

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IROS Conference 2013 Conference Paper

Optimal planning for information acquisition

Yonatan Silverman
Lauren M. Miller
Malcolm A. MacIver
Todd D. Murphey

This paper presents an algorithm for active search where the goal is to calculate optimal trajectories for autonomous robots during data acquisition tasks. Formulating the problem as parameter estimation enables us to use Fisher information to create an explicit connection between robot dynamics and the informative regions of the search space. We use optimal control to automate design of trajectories that spend time in regions proportional to the probability of collecting informative data and use acquired data to update the probability closed-loop. Experimental and simulated results use a robotic electrosense platform to localize a feature in one-dimension. We demonstrate that this method is robust with respect to disturbances and initial conditions, and results in successful localization of the feature with a 100% experimental success rate and a 34% reduction in localization time compared to the next best tested controller.

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IROS Conference 2012 Conference Paper

Location and orientation estimation with an electrosense robot

Yonatan Silverman
James Snyder
Yang Bai
Malcolm A. MacIver

We have designed an underwater robot that uses perturbations of an emitted electric field to sense, localize, and map its environment. This system is inspired by weakly electric fish, which emit an electric field to sense objects, localize prey, and communicate. When nearby objects distort the electric field, electroreceptors (fish) or voltage sensors (robot) detect these perturbations. Further analysis of the perturbations can reveal information about the associated target, such as size, shape, and distance. One difficulty with extracting distance-to-target for our robotic electrosense platform is that the measurements are dependent on orientation of the robot with respect to the object. We solve this problem by applying techniques from range-only SLAM, with modifications for some of the ways in which electrosense differs from the sensors typically used. We use two different Bayesian filters to estimate the orientation and position separately. Using this approach, we show that our electrosense robot can accurately localize and orient itself, and improve its estimate of position and orientation using motion.

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IROS Conference 2012 Conference Paper

Sensing capacitance of underwater objects in bio-inspired electrosense

Yang Bai
James Snyder
Yonatan Silverman
Michael A. Peshkin
Malcolm A. MacIver

Certain electric fish use a self-generated AC electric field to navigate and hunt. Thousands of sensors on the surface of the fish's body detect the pattern of amplitude and phase distortions of the field caused by nearby objects. Prior research has suggested that phase distortions may be especially useful for recognition of live objects. Here we present the first study of the utility of phase information in a robotic implementation of active electrosense. Using our robotic implementation, we investigated how the phase information depends on the frequency of the emitted field, the conductivity of the surrounding water, and object properties. An analytical model was developed serving as qualitative explanation of the dependency. We show that in certain situations phase information enables discrimination between two objects that are otherwise very similar in the amplitude of their electric images. We also show the utility of probing objects with multiple frequencies.

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IROS Conference 2012 Conference Paper

Underwater object tracking using electrical impedance tomography

James Snyder
Yonatan Silverman
Yang Bai
Malcolm A. MacIver

Few effective technologies exist for sensing in dark or murky underwater situations. For this reason, we have been exploring the use of a novel biologically-inspired approach to non-visual sensing based on the detection of perturbations to a self generated electric field. This is used by many species of neotropical nocturnal freshwater fish. This approach, termed active electrosense, presents unique challenges for sensing and tracking of nearby objects. We explore two methods for estimating the velocity of objects through active electrosense. The first of these methods uses a simple cross-correlation method, which depends on the uniformity of the electric field. We show some of the ramifications of making this assumption for a self-generated field around a cylindrical pod-shaped sensor in a rectangular tank. We then evaluate the use of methods developed for electrical impedance tomography (EIT) for localization and tracking. This is an unusual application of EIT in that typical applications involve surrounding the volume of interest (such as the thorax of humans) with sensor/emitters. Here, rather than this “outside in” approach, we are using EIT “inside out. ” In simulation, we nonetheless find significant improvements in the accuracy of estimated velocity when using the EIT approach. Additionally, we demonstrate how EIT may be used for accurate position estimation. Under the conditions evaluated, the computation time for inversion is low enough to make its use feasible as a primary position and velocity estimator in an on- line system or as a secondary system to augment a computationally inexpensive estimator.

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ICRA Conference 2007 Conference Paper

Robotic Electrolocation: Active Underwater Target Localization with Electric Fields

James R. Solberg
Kevin M. Lynch
Malcolm A. MacIver

We explore the capabilities of a robot designed to locate objects underwater through active movement of an electric field emitter and sensor apparatus. The robot is inspired by the biological phenomenon of active electrolocation, a sensing strategy found in two groups of freshwater fishes known to emit weak electric fields for target localization and communication. We characterize the performance of the robot using several types of automatic electrolocation controllers, objects, and water conditions. We demonstrate successful electrolocation both in the conditions in which it is naturally observed, in low conductivity water, as well as in conditions in which it is not observed, in water of ocean salinity. The belief of the position of the target is maintained via a particle filter and refined with each measurement

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IROS Conference 2006 Conference Paper

Generating Thrust with a Biologically-Inspired Robotic Ribbon Fin

Michael Epstein
J. Edward Colgate
Malcolm A. MacIver

We present experimental results of thrust produced by a robotic propulsor, the design of which is inspired by the ribbon fin of the South American black ghost knifefish (Apteronotus albifrons). This remarkably nimble fish moves by oscillating its ribbon fin rays out of phase and thereby passing a traveling wave along the fin's length. Combinations of thrust from the ribbon fin and body rolls produced by the two pectoral fins enable the black ghost to swim in nearly any direction without bending its body. The fish's agile locomotor system is tightly integrated with its omnidirectional, active sensing system. The robotic ribbon fin has eight individually actuated metal rays which are linked by a thin latex sheet. The experimental results demonstrate the effect of varying the propulsive wave's frequency, amplitude and length on the robotic fin's thrust production. We found that thrust production peaks at particular combinations of the three variables and that the fin could produce steady forward thrust, despite the relatively small number of rays. The robotic ribbon fin has potential application as a propulsor for future underwater vehicles, in addition to being a valuable scientific instrument in understanding the swimming mechanics of the black ghost and similar fish

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