Studying the Effects of Robot Intervention on School Shooters in Virtual Reality
- URL: http://arxiv.org/abs/2510.17948v1
- Date: Mon, 20 Oct 2025 17:42:24 GMT
- Title: Studying the Effects of Robot Intervention on School Shooters in Virtual Reality
- Abstract summary: We conduct a virtual reality study with 150 university participants role-playing as a school shooter.<n>Within the simulation, an autonomous robot predicted the shooter's movements and positioned itself strategically to interfere and distract.<n>An aggressive, high-distraction robot reduced the number of victims by 46.6% relative to a no-robot control.
- Score: 2.2774471443318753
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We advance the understanding of robotic intervention in high-risk scenarios by examining their potential to distract and impede a school shooter. To evaluate this concept, we conducted a virtual reality study with 150 university participants role-playing as a school shooter. Within the simulation, an autonomous robot predicted the shooter's movements and positioned itself strategically to interfere and distract. The strategy the robot used to approach the shooter was manipulated -- either moving directly in front of the shooter (aggressive) or maintaining distance (passive) -- and the distraction method, ranging from no additional cues (low), to siren and lights (medium), to siren, lights, and smoke to impair visibility (high). An aggressive, high-distraction robot reduced the number of victims by 46.6% relative to a no-robot control. This outcome underscores both the potential of robotic intervention to enhance safety and the pressing ethical questions surrounding their use in school environments.
Related papers
- ActiveMimic: Egocentric Video Pretraining with Active Perception [84.2999803878421]
Egocentric human video offers a scalable alternative to robot data for pretraining.<n>We present ActiveMimic, a pretraining framework that recovers synchronized camera and wrist trajectories from a single body-worn RGB camera.<n>We show that ActiveMimic consistently surpasses baselines pretrained on human video and matches state-of-the-art models pretrained on robot data.
arXiv Detail & Related papers (2026-06-04T14:01:01Z) - Robot Crash Course: Learning Soft and Stylized Falling [7.959692704349906]
We aim to reduce physical damage to the robot while providing users with control over a robot's end pose.<n>Our work demonstrates that even bipedal robots can perform controlled, soft falls.
arXiv Detail & Related papers (2025-11-13T18:55:34Z) - A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference.
We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data.
Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
arXiv Detail & Related papers (2024-11-08T17:20:47Z) - Deception Game: Closing the Safety-Learning Loop in Interactive Robot
Autonomy [7.915956857741506]
Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior.
This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty.
arXiv Detail & Related papers (2023-09-03T20:34:01Z) - Robot Learning with Sensorimotor Pre-training [98.7755895548928]
We present a self-supervised sensorimotor pre-training approach for robotics.
Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens.
We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
arXiv Detail & Related papers (2023-06-16T17:58:10Z) - Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning [26.13655448415553]
Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot.
We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game.
The resulting agent exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more.
arXiv Detail & Related papers (2023-04-26T16:25:54Z) - Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills
using a Quadrupedal Robot [76.04391023228081]
We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning.
We propose a hierarchical framework that leverages deep reinforcement learning to train a robust motion control policy.
We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.
arXiv Detail & Related papers (2022-08-01T22:34:51Z) - Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot
Learning [121.9708998627352]
Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off.
This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
arXiv Detail & Related papers (2022-04-15T08:12:15Z) - Intention Aware Robot Crowd Navigation with Attention-Based Interaction
Graph [3.8461692052415137]
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
We propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents.
We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios.
arXiv Detail & Related papers (2022-03-03T16:26:36Z) - Dual-Arm Adversarial Robot Learning [0.6091702876917281]
We propose dual-arm settings as platforms for robot learning.
We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
arXiv Detail & Related papers (2021-10-15T12:51:57Z) - SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects.
We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
arXiv Detail & Related papers (2020-03-19T00:11:34Z) - Morphology-Agnostic Visual Robotic Control [76.44045983428701]
MAVRIC is an approach that works with minimal prior knowledge of the robot's morphology.
We demonstrate our method on visually-guided 3D point reaching, trajectory following, and robot-to-robot imitation.
arXiv Detail & Related papers (2019-12-31T15:45:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.