arXiv subjects

Allison Okamura

Publications and source records attributed to Allison Okamura.

9 recordsLinked to original sources

arXiv2026

PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot

Vine robots, a class of soft, growing robots, are suitable for navigating complex and confined environments due to their compliant bodies and self-supporting growth mechanism. However, hysteresis, tether interactions, and deformations make them difficult to predict and model, which in turn limits the effectiveness of conventional planning and control approaches. In this work, we present a data-driven, vision-based control framework for the first autonomous vine robot system. Our system integrates 19 cameras distributed along the robot's body to provide comprehensive feedback of both the robot state and the surrounding environment. Using this rich whole-body vision feedback, we train an end-to-end visuomotor policy from demonstrations for closed-loop autonomous control in complex environments. The policy efficiently aggregates information from distributed sensing while maintaining robustness to inaccurate robot states and actuation. Experimental results demonstrate that the learned policy enables robust navigation and manipulation in challenging scenarios, including steering through branched structures, climbing up slopes, traversing unsupported terrain, reaching objects precisely, and maneuvering through confined spaces and obstacles. Project website https://panovine-bot.github.io

cs.RO↗

arXiv2026

Soft 3D Metamaterial for Low-Frequency Elastic Waves

Acoustic metamaterials offer exceptional control over wave propagation, but their potential remains unfulfilled due to fabrication constraints. Conventional processes yield mostly rigid, planar structures, whereas soft-matter alternatives have so far been confined to ultrasounds. This work overcomes prior limitations with a fully soft 3D metamaterial operating around 200Hz. The design combines a 3D-printed elastomer lattice with resonant inclusions of liquid metal, injected via a network of mesofluidic channels. Its dynamic response is derived from a hybrid strategy uniting a lumped-element model with finite element analysis. Simulations reveal how the dual-phase design decouples flexural and torsional modes, opening a subwavelength band gap for low-frequency elastic waves. Empirical validation is achieved via a custom camera-based vibrometer. Its high spatiotemporal resolution and full-field capabilities enable direct capture of local modes and evanescent waves underlying the band gap. Accelerometer data corroborate these findings and demonstrate greater attenuation than common silicone elastomers at only half of the density. By combining scalable fabrication, compliance, and operations at frequencies relevant to human tactile perception, this novel metamaterial paves the way for lightweight, high-performance cushioning and handles that protect users from harmful vibration exposure.

cond-mat.soft↗

arXiv2025

Effect of Performance Feedback Timing on Motor Learning for a Surgical Training Task

Objective: Robot-assisted minimally invasive surgery (RMIS) has become the gold standard for a variety of surgical procedures, but the optimal method of training surgeons for RMIS is unknown. We hypothesized that real-time, rather than post-task, error feedback would better increase learning speed and reduce errors. Methods: Forty-two surgical novices learned a virtual version of the ring-on-wire task, a canonical task in RMIS training. We investigated the impact of feedback timing with multi-sensory (haptic and visual) cues in three groups: (1) real-time error feedback, (2) trial replay with error feedback, and (3) no error feedback. Results: Participant performance was evaluated based on the accuracy of ring position and orientation during the task. Participants who received real-time feedback outperformed other groups in ring orientation. Additionally, participants who received feedback in replay outperformed participants who did not receive any error feedback on ring orientation during long, straight path sections. There were no significant differences between groups for ring position overall, but participants who received real-time feedback outperformed the other groups in positional accuracy on tightly curved path sections. Conclusion: The addition of real-time haptic and visual error feedback improves learning outcomes in a virtual surgical task over error feedback in replay or no error feedback at all. Significance: This work demonstrates that multi-sensory error feedback delivered in real time leads to better training outcomes as compared to the same feedback delivered after task completion. This novel method of training may enable surgical trainees to develop skills with greater speed and accuracy.

cs.RO↗

arXiv2024

Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment

For decades, robotics researchers have pursued various tasks for multi-robot systems, from cooperative manipulation to search and rescue. These tasks are multi-robot extensions of classical robotic tasks and often optimized on dimensions such as speed or efficiency. As robots transition from commercial and research settings into everyday environments, social task aims such as engagement or entertainment become increasingly relevant. This work presents a compelling multi-robot task, in which the main aim is to enthrall and interest. In this task, the goal is for a human to be drawn to move alongside and participate in a dynamic, expressive robot flock. Towards this aim, the research team created algorithms for robot movements and engaging interaction modes such as gestures and sound. The contributions are as follows: (1) a novel group navigation algorithm involving human and robot agents, (2) a gesture responsive algorithm for real-time, human-robot flocking interaction, (3) a weight mode characterization system for modifying flocking behavior, and (4) a method of encoding a choreographer's preferences inside a dynamic, adaptive, learned system. An experiment was performed to understand individual human behavior while interacting with the flock under three conditions: weight modes selected by a human choreographer, a learned model, or subset list. Results from the experiment showed that the perception of the experience was not influenced by the weight mode selection. This work elucidates how differing task aims such as engagement manifest in multi-robot system design and execution, and broadens the domain of multi-robot tasks.

cs.RO↗

arXiv2023

Music Mode: Transforming Robot Movement into Music Increases Likability and Perceived Intelligence

As robots enter everyday spaces like offices, the sounds they create affect how they are perceived. We present Music Mode, a novel mapping between a robot's joint motions and sounds, programmed by artists and engineers to make the robot generate music as it moves. Two experiments were designed to characterize the effect of this musical augmentation on human users. In the first experiment, a robot performed three tasks while playing three different sound mappings. Results showed that participants observing the robot perceived it as more safe, animate, intelligent, anthropomorphic, and likable when playing the Music Mode Orchestra software. To test whether the results of the first experiment were due to the Music Mode algorithm, rather than music alone, we conducted a second experiment. Here the robot performed the same three tasks, while a participant observed via video, but the Orchestra music was either linked to its movement or random. Participants rated the robots as more intelligent when the music was linked to the movement. Robots using Music Mode logged approximately two hundred hours of operation while navigating, wiping tables, and sorting trash, and bystander comments made during this operating time served as an embedded case study. This paper has both designerly contributions and engineering contributions. The contributions are: (1) an interdisciplinary choreographic, musical, and coding design process to develop a real-world robot sound feature, (2) a technical implementation for movement-based sound generation, and (3) two experiments and an embedded case study of robots running this feature during daily work activities that resulted in increased likeability and perceived intelligence of the robot.

cs.RO↗

arXiv2022

Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models

Learning from demonstration is a proven technique to teach robots new skills. Data quality and quantity play a critical role in the performance of models trained using data collected from human demonstrations. In this paper we enhance an existing teleoperation data collection system with real-time haptic feedback to the human demonstrators; we observe improvements in the collected data throughput and in the performance of autonomous policies using models trained with the data. Our experimental testbed was a mobile manipulator robot that opened doors with latch handles. Evaluation of teleoperated data collection on eight real conference room doors found that adding haptic feedback improved data throughput by 6%. We additionally used the collected data to train six image-based deep imitation learning models, three with haptic feedback and three without it. These models were used to implement autonomous door-opening with the same type of robot used during data collection. A policy from a imitation learning model trained with data collected while the human demonstrators received haptic feedback performed on average 11% better than its counterpart trained with data collected without haptic feedback, indicating that haptic feedback provided during data collection resulted in improved autonomous policies.

cs.RO↗

arXiv2022

Hoxels: Fully 3-D Printed Soft Multi-Modal & Multi-Contact Haptic Voxel Displays for Enriched Tactile Information Transfer

Wrist-worn haptic interfaces can deliver a wide range of tactile cues for communication of information and interaction with virtual objects. Unlike fingertips, the wrist and forearm provide a considerably large area of skin that allows the placement of multiple haptic actuators as a display for enriching tactile information transfer with minimal encumbrance. Existing multi-degree-of-freedom (DoF) wrist-worn devices employ traditional rigid robotic mechanisms and electric motors that limit their versatility, miniaturization, distribution, and assembly. Alternative solutions based on soft elastomeric actuator arrays constitute only 1-DoF haptic pixels. Higher-DoF prototypes produce a single interaction point and require complex manual assembly processes, such as molding and gluing several parts. These approaches limit the construction of high-DoF compact haptic displays, repeatability, and customizability. Here we present a novel, fully 3D-printed, soft, wearable haptic display for increasing tactile information transfer on the wrist and forearm with 3-DoF haptic voxels, called hoxels. Our initial prototype comprises two hoxels that provide skin shear, pressure, twist, stretch, squeeze, and other arbitrary stimuli. Each hoxel generates force up to 1.6 N in the x and y-axes and up to 20 N in the z-axis. Our method enables the rapid fabrication of versatile and forceful haptic displays.

cs.RO↗

arXiv2020

A Dynamics Simulator for Soft Growing Robots

Simulating soft robots in cluttered environments remains an open problem due to the challenge of capturing complex dynamics and interactions with the environment. Furthermore, fast simulation is desired for quickly exploring robot behaviors in the context of motion planning. In this paper, we examine a particular class of inflated-beam soft growing robots called "vine robots", and present a dynamics simulator that captures general behaviors, handles robot-object interactions, and runs faster than real time. The simulator framework uses a simplified multi-link, rigid-body model with contact constraints. To narrow the sim-to-real gap, we develop methods for fitting model parameters based on video data of a robot in motion and in contact with an environment. We provide examples of simulations, including several with fit parameters, to show the qualitative and quantitative agreement between simulated and real behaviors. Our work demonstrates the capabilities of this high-speed dynamics simulator and its potential for use in the control of soft robots.

cs.RO↗

arXiv2016

Next Generation Robotics

The National Robotics Initiative (NRI) was launched 2011 and is about to celebrate its 5 year anniversary. In parallel with the NRI, the robotics community, with support from the Computing Community Consortium, engaged in a series of road mapping exercises. The first version of the roadmap appeared in September 2009; a second updated version appeared in 2013. While not directly aligned with the NRI, these road-mapping documents have provided both a useful charting of the robotics research space, as well as a metric by which to measure progress. This report sets forth a perspective of progress in robotics over the past five years, and provides a set of recommendations for the future. The NRI has in its formulation a strong emphasis on co-robot, i.e., robots that work directly with people. An obvious question is if this should continue to be the focus going forward? To try to assess what are the main trends, what has happened the last 5 years and what may be promising directions for the future a small CCC sponsored study was launched to have two workshops, one in Washington DC (March 5th, 2016) and another in San Francisco, CA (March 11th, 2016). In this report we brief summarize some of the main discussions and observations from those workshops. We will present a variety of background information in Section 2, and outline various issues related to progress over the last 5 years in Section 3. In Section 4 we will outline a number of opportunities for moving forward. Finally, we will summarize the main points in Section 5.

cs.CY↗