arXiv subjects

Steven M. LaValle

Publications and source records attributed to Steven M. LaValle.

At least 19 recordsLinked to original sources

arXiv2026

Uncovering EEG Patterns Consistently Associated with Cybersickness Discomfort Using Deep Learning Interpretability Maps

Uncomfortable sensations similar to motion sickness, called cybersickness, can develop when using Virtual Reality (VR) head-mounted displays. Cybersickness poses a hindrance to greater use of VR technology. Brain activity recorded using electroencephalogram (EEG) can be used to unintrusively detect cybersickness and the discomfort it causes. To intervene for mitigation, machine learning algorithms that can extract meaningful signals related to cybersickness discomfort from the rest of the brain data will be required. In this work, we determined which features in EEG data were most helpful for classifying cybersickness-related discomfort by building a framework with neural networks and interpretability maps. Using brain data from two separate auditory event-related potential (ERP) cybersickness user studies, we extracted which spatio-temporal EEG features (from sensor locations and time steps) were most important for discomfort classification. For the first dataset (n=29), across 120 runs of our framework with three different neural networks over multiple random seeds, the models consistently pointed to scalp locations in left frontal, midline central, and right parietal areas as most helpful in determining if EEG data belonged to someone who was experiencing cybersickness discomfort. Similar locations were also tagged in a second dataset (n=36), showing cross-dataset generalizability of our findings across time, VR stimulus, and participant sample. Early periods in the extracted ERP time windows, approximately between 80 and 260 milliseconds post-stimulus, were marked as important for model classification in both datasets. These results help clarify which tagged features can be used for cybersickness-related (and potentially other types of) discomfort classification with EEG in the future. Project webpage at: https://eeg-discomfort-nnilc.github.io/.

cs.HC↗

arXiv2026

The Minkowski Wrap: A Relativistic Speed Limiter

In special relativity, a particle can experience constant acceleration, but at the same time, its motion is constrained by the velocity limit imposed by the speed of light $c$. Inspired by this principle, we propose a method for enforcing velocity bounds in control systems by replacing $c$ with the maximum attainable speed of the system. We refer to this as the ``Minkowski wrap," the operation of deforming the phase portrait of a system so as to enforce desired speed limits. We apply this idea to shape the input generated by a state-feedback stabilizing controller and time-optimal controller considering controlling a double integrator system. By applying Pontryagin's Maximum Principle, we show that the time-optimal control of a wrapped double-integrator system is bang-bang. The proposed method transforms the classical double-integrator dynamics into a ``wrapped" system that respects velocity bounds without the need for clipping, offering an explicit nonlinear feedback control strategy conducive to safety applications.

eess.SY↗

arXiv2026

Relating Reinforcement Learning to Dynamic Programming-Based Planning

This paper bridges some of the gap between optimal planning and reinforcement learning (RL), both of which share roots in dynamic programming applied to sequential decision making or optimal control. Whereas planning typically favors deterministic models, goal termination, and cost minimization, RL tends to favor stochastic models, infinite-horizon discounting, and reward maximization in addition to learning-related parameters such as the learning rate and greediness factor. A derandomized version of RL is developed, analyzed, and implemented to yield performance comparisons with value iteration and Dijkstra's algorithm using simple planning models. Next, mathematical analysis shows: 1) conditions under which cost minimization and reward maximization are equivalent, 2) conditions for equivalence of single-shot goal termination and infinite-horizon episodic learning, and 3) conditions under which discounting causes goal achievement to fail. The paper then advocates for defining and optimizing truecost, rather than inserting arbitrary parameters to guide operations. Performance studies are then extended to the stochastic case, using planning-oriented criteria and comparing value iteration to RL with learning rates and greediness factors.

cs.RO↗

arXiv2026

Planning Smooth and Safe Control Laws for a Unicycle Robot Among Obstacles

This paper presents a framework for safe navigation of a unicycle point robot to a goal position in an environment populated with obstacles from almost any admissible state, considering input limits. We introduce a novel QP formulation to create a Cinfinity-smooth vector field with reduced total bending and total turning. Then we design an analytic, non-linear feedback controller that inherently satisfies the conditions of Nagumo's theorem, ensuring forward invariance of the safe set without requiring any online optimization. We have demonstrated that our controller, even under hard input limits, safely converges to the goal position. Simulations confirm the effectiveness of the proposed framework, resulting in a twice faster arrival time with over 50\% lower angular control effort compared to the baseline.

cs.RO↗

arXiv2026

Smooth Feedback Motion Planning with Reduced Curvature

Feedback motion planning over cell decompositions provides a robust method for generating collision-free robot motion with formal guarantees. However, existing algorithms often produce paths with unnecessary bending, leading to slower motion and higher control effort. This paper presents a computationally efficient method to mitigate this issue for a given simplicial decomposition. A heuristic is introduced that systematically aligns and assigns local vector fields to produce more direct trajectories, complemented by a novel geometric algorithm that constructs a maximal star-shaped chain of simplexes around the goal. This creates a large ``funnel'' in which an optimal, direct-to-goal control law can be safely applied. Simulations demonstrate that our method generates measurably more direct paths, reducing total bending by an average of 91.40\% and LQR control effort by an average of 45.47\%. Furthermore, comparative analysis against sampling-based and optimization-based planners confirms the time efficacy and robustness of our approach. While the proposed algorithms work over any finite-dimensional simplicial complex embedded in the collision-free subset of the configuration space, the practical application focuses on low-dimensional ($d\le3$) configuration spaces, where simplicial decomposition is computationally tractable.

cs.RO↗

arXiv2026

Mobile Robot Localization Using a Novel Whisker-Like Sensor

Whisker-like touch sensors offer unique advantages for short-range perception in environments where visual and long-range sensing are unreliable, such as confined, cluttered, or low-visibility settings. This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor. We develop a family of virtual sensor models. Each model maps robot configurations to sensor observations and enables structured reasoning through the concept of preimages - the set of robot states consistent with a given observation. The notion of virtual sensor models serves as an abstraction to reason about state uncertainty without dependence on physical implementation. By combining sensor observations with a motion model, we estimate the contact point. Iterative estimation then enables reconstruction of obstacle boundaries. Furthermore, intersecting states inferred from current observations with forward-projected states from previous steps allow accurate robot localization without relying on vision or external systems. The framework supports both deterministic and possibilistic formulations and is validated through simulation and physical experiments using a low-cost, 3D printed, Hall-effect-based whisker sensor. Results demonstrate accurate contact estimation and localization with errors under 7 mm, demonstrating the potential of whisker-based sensing as a lightweight, adaptable complement to vision-based navigation.

physics.app-ph↗

arXiv2025

Unwinding Rotations Reduces VR Sickness in Nonsimulated Immersive Telepresence

Immersive telepresence, when a user views the video stream of a $360^\circ$ camera in a remote environment using a Head Mounted Display (HMD), has great potential to improve the sense of being in a remote environment. In most cases of immersive robotic telepresence, the camera is mounted on a mobile robot which increases the portion of the environment that the remote user can explore. However, robot motions can induce unpleasant symptoms associated with Virtual Reality (VR) sickness, degrading the overall user experience. Previous research has shown that unwinding the rotations of the robot, that is, decoupling the rotations that the camera undergoes due to robot motions from what is seen by the user, can increase user comfort and reduce VR sickness. However, that work considered a virtual environment and a simulated robot. In this work, to test whether the same hypotheses hold when the video stream from a real camera is used, we carried out a user study $(n=36)$ in which the unwinding rotations method was compared against coupled rotations in a task completed through a panoramic camera mounted on a robotic arm. Furthermore, within an inspection task which involved translations and rotations in three dimensions, we tested whether unwinding the robot rotations impacted the performance of users. The results show that the users found the unwinding rotations method to be more comfortable and preferable, and that a reduced level of VR sickness can be achieved without a significant impact on task performance.

cs.RO↗

arXiv2025

Sharing but Not Caring: Similar Outcomes for Shared Control and Switching Control in Telepresence-Robot Navigation

Telepresence robots enable users to interact with remote environments, but efficient and intuitive navigation remains a challenge. In this work, we developed and evaluated a shared control method, in which the robot navigates autonomously while allowing users to affect the path generation to better suit their needs. We compared this with control switching, where users toggle between direct and automated control. We hypothesized that shared control would maintain efficiency comparable to control switching while potentially reducing user workload. The results of two consecutive user studies (each with final sample of n=20) showed that shared control does not degrade navigation efficiency, but did not show a significant reduction in task load compared to control switching. Further research is needed to explore the underlying factors that influence user preference and performance in these control systems.

cs.RO↗

arXiv2025

Body Ownership Affects the Processing of Sensorimotor Contingencies in Virtual Reality

Presence in virtual reality (VR), the subjective sense of "being there" in a virtual environment, is notoriously difficult to measure. Electroencephalography (EEG) may offer a promising, unobtrusive means of assessing a user's momentary state of presence. Unlike traditional questionnaires, EEG does not interrupt the experience or rely on users' retrospective self-reports, thereby avoiding interference with the very state it aims to capture. Previous research has attempted to quantify presence in virtual environments using event-related potentials (ERPs). We contend, however, that previous efforts have fallen short of fully realizing this goal, failing to either A) independently manipulate presence, B) validate their measure of presence against traditional techniques, C) adequately separate the constructs of presence and attention, and/or D) implement a realistic and immersive environment and task. We address these shortcomings in a preregistered ERP experiment in which participants play an engaging target shooting game in VR. ERPs are time-locked to the release of a ball from a sling. We induce breaks in presence (BIPs) by freezing the ball's release on a minority of trials. Embodiment is manipulated by allowing manual manipulation of the sling with a realistic avatar in one condition (embodied condition) and passive manipulation with only controllers in another (non-embodied condition). We support our predictions that the N2, the P3b, and the N400, are selectively sensitive towards specific components of these manipulations. The pattern of findings carries significant implications for theories of presence, which have been seldom addressed in previous ERP investigations on this topic.

cs.HC↗

arXiv2025

Deformation of the panoramic sphere into an ellipsoid to induce self-motion in telepresence users

Mobile telepresence robots allow users to feel present and explore remote environments using technology. Traditionally, these systems are implemented using a camera onboard a mobile robot that can be controlled. Although high-immersion technologies, such as 360-degree cameras, can increase situational awareness and presence, they also introduce significant challenges. Additional processing and bandwidth requirements often result in latencies of up to seconds. The current delay with a 360-degree camera streaming over the internet makes real-time control of these systems difficult. Working with high-latency systems requires some form of assistance to the users. This study presents a novel way to utilize optical flow to create an illusion of self-motion to the user during the latency period between user sending motion commands to the robot and seeing the actual motion through the 360-camera stream. We find no significant benefit of using the self-motion illusion to performance or accuracy of controlling a telepresence robot with a latency of 500 ms, as measured by the task completion time and collisions into objects. Some evidence is shown that the method might increase virtual reality (VR) sickness, as measured by the simulator sickness questionnaire (SSQ). We conclude that further adjustments are necessary in order to render the method viable.

cs.RO↗

arXiv2025

Optimal Control of Sensor-Induced Illusions on Robotic Agents

This paper presents a novel problem of creating and regulating localization and navigation illusions considering two agents: a receiver and a producer. A receiver is moving on a plane localizing itself using the intensity of signals from three known towers observed at its position. Based on this position estimate, it follows a simple policy to reach its goal. The key idea is that a producer alters the signal intensities to alter the position estimate of the receiver while ensuring it reaches a different destination with the belief that it reached its goal. We provide a precise mathematical formulation of this problem and show that it allows standard techniques from control theory to be applied to generate localization and navigation illusions that result in a desired receiver behavior.

cs.RO↗

arXiv2025

Minimally sufficient structures for information-feedback policies

In this paper, we consider robotic tasks which require a desirable outcome to be achieved in the physical world that the robot is embedded in and interacting with. Accomplishing this objective requires designing a filter that maintains a useful representation of the physical world and a policy over the filter states. A filter is seen as the robot's perspective of the physical world based on limited sensing, memory, and computation and it is represented as a transition system over a space of information states. To this end, the interactions result from the coupling of an internal and an external system, a filter, and the physical world, respectively, through a sensor mapping and an information-feedback policy. Within this setup, we look for sufficient structures, that is, sufficient internal systems and sensors, for accomplishing a given task. We establish necessary and sufficient conditions for these structures to satisfy for information-feedback policies that can be defined over the states of an internal system to exist. We also show that under mild assumptions, minimal internal systems that can represent a particular plan/policy described over the action-observation histories exist and are unique. Finally, the results are applied to determine sufficient structures for distance-optimal navigation in a polygonal environment.

cs.RO↗

arXiv2024

Look-and-Twist: A Simple Selection Method for Virtual and Augmented Reality

This paper introduces a novel interaction method for virtual and augmented reality called look-and-twist, which is directly analogous to point-and-click operations using a mouse and desktop. It is based on head rotation alone and is straightforward to implement on any head mounted display that performs rotational tracking. A user selects features of interest by turning their head to face an object, and then performs a specified rotation along the axis of the looking direction. The look-and-twist method has been implemented and tested in an educational context, and systematic user studies are underway. Early evidence indicates that the method is comparable to, or faster than, the standard dwell time method. The method can be used, for example, with Google Cardboard, and it is straightforward to learn for inexperienced users. Moreover, it has the potential to significantly enrich VR interactions by providing an additional degree of freedom of control, which the binary nature of dwell-based methods lacks.

cs.HC↗

arXiv2024

Universal Plans: One Action Sequence to Solve Them All!

This paper introduces the notion of a universal plan, which when executed, is guaranteed to solve all planning problems in a category, regardless of the obstacles, initial state, and goal set. Such plans are specified as a deterministic sequence of actions that are blindly applied without any sensor feedback. Thus, they can be considered as pure exploration in a reinforcement learning context, and we show that with basic memory requirements, they even yield optimal plans. Building upon results in number theory and theory of automata, we provide universal plans both for discrete and continuous (motion) planning and prove their (semi)completeness. The concepts are applied and illustrated through simulation studies, and several directions for future research are sketched.

cs.RO↗

arXiv2024

An Internal Model Principle For Robots

When designing a robot's internal system, one often makes assumptions about the structure of the intended environment of the robot. One may even assign meaning to various internal components of the robot in terms of expected environmental correlates. In this paper we want to make the distinction between robot's internal and external worlds clear-cut. Can the robot learn about its environment, relying only on internally available information, including the sensor data? Are there mathematical conditions on the internal robot system which can be internally verified and make the robot's internal system mirror the structure of the environment? We prove that sufficiency is such a mathematical principle, and mathematically describe the emergence of the robot's internal structure isomorphic or bisimulation equivalent to that of the environment. A connection to the free-energy principle is established, when sufficiency is interpreted as a limit case of surprise minimization. As such, we show that surprise minimization leads to having an internal model isomorphic to the environment. This also parallels the Good Regulator Principle which states that controlling a system sufficiently well means having a model of it. Unlike the mentioned theories, ours is discrete, and non-probabilistic.

cs.RO↗

arXiv2024

Localization with Single or Antipodal Distance Measurements

Given a polygonal workspace $W$, a depth sensor placed at point $p=(x,y)$ inside $W$ and oriented in direction $θ$ measures the distance $d=h(x,y,θ)$ between $p$ and the closest point on the boundary of $W$ along a ray emanating from $p$ in direction $θ$. We study the following problem: For a polygon $W$ with $n$ vertices, possibly with holes, preprocess it such that given a query real value $d> 0$, one can efficiently compute the preimage $h^{-1}(d) \subset W\times \mathbb{S}^1$, namely determine all the possible poses (positions and orientations) of a depth sensor placed in $W$ that would yield the reading $d$, in an output-sensitive fashion. We describe such an output-sensitive data structure, which answers queries in $O(k \log n)$ time, where $k$ is the number of vertices and maximal arcs of low degree algebraic curves constituting the answer. We also obtain analogous results for the more useful case (narrowing down the set of possible poses), where the sensor performs two antipodal depth measurements from the same point in $W$. We then describe simpler data structures for the same two problems, where we employ a decomposition of $W\times \mathbb{S}^1$, and where the query time is output-sensitive relative to this decomposition. Our software implementation for these latter structures is open source and publicly available. Although robot localization is often carried out by exploring the full visibility polygon of a sensor placed at a point of the environment, the approach that we propose here opens the door to sufficing with only few depth measurements, which is advantageous as it allows for usage of inexpensive sensors and could also lead to savings in storage and communication costs.

cs.CG↗

arXiv2024

From Virtual Reality to the Emerging Discipline of Perception Engineering

This paper makes the case that a powerful new discipline, which we term perception engineering, is steadily emerging. It follows from a progression of ideas that involve creating illusions, from historical paintings and film, to video games and virtual reality in modern times. Rather than creating physical artifacts such as bridges, airplanes, or computers, perception engineers create illusory perceptual experiences. The scope is defined over any agent that interacts with the physical world, including both biological organisms (humans, animals) and engineered systems (robots, autonomous systems). The key idea is that an agent, called a producer, alters the environment with the intent to alter the perceptual experience of another agent, called a receiver. Most importantly, the paper introduces a precise mathematical formulation of this process, based on the von Neumann-Morgenstern notion of information, to help scope and define the discipline. It is then applied to the cases of engineered and biological agents with discussion of its implications on existing fields such as virtual reality, robotics, and even social media. Finally, open challenges and opportunities for involvement are identified.

cs.HC↗

arXiv2024

Equivalent Environments and Covering Spaces for Robots

This paper formally defines a robot system, including its sensing and actuation components, as a general, topological dynamical system. The focus is on determining general conditions under which various environments in which the robot can be placed are indistinguishable. A key result is that, under very general conditions, covering maps witness such indistinguishability. This formalizes the intuition behind the well studied loop closure problem in robotics. An important special case is where the sensor mapping reports an invariant of the local topological (metric) structure of an environment because such structure is preserved by (metric) covering maps. Whereas coverings provide a sufficient condition for the equivalence of environments, we also give a necessary condition using bisimulation. The overall framework is applied to unify previously identified phenomena in robotics and related fields, in which moving agents with sensors must make inferences about their environments based on limited data. Many open problems are identified.

cs.RO↗