Multi-Agent Active Search using Realistic Depth-Aware Noise Model
- URL: http://arxiv.org/abs/2011.04825v2
- Date: Mon, 22 Mar 2021 16:39:19 GMT
- Title: Multi-Agent Active Search using Realistic Depth-Aware Noise Model
- Authors: Ramina Ghods, William J. Durkin, Jeff Schneider
- Abstract summary: Active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers.
Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored.
We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robots performing active search considering two sources of sensory information from monocular optical imagery and depth maps.
- Score: 8.520962086877548
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The active search for objects of interest in an unknown environment has many
robotics applications including search and rescue, detecting gas leaks or
locating animal poachers. Existing algorithms often prioritize the location
accuracy of objects of interest while other practical issues such as the
reliability of object detection as a function of distance and lines of sight
remain largely ignored. Additionally, in many active search scenarios,
communication infrastructure may be unreliable or unestablished, making
centralized control of multiple agents impractical. We present an algorithm
called Noise-Aware Thompson Sampling (NATS) that addresses these issues for
multiple ground-based robots performing active search considering two sources
of sensory information from monocular optical imagery and depth maps. By
utilizing Thompson Sampling, NATS allows for decentralized coordination among
multiple agents. NATS also considers object detection uncertainty from depth as
well as environmental occlusions and operates while remaining agnostic of the
number of objects of interest. Using simulation results, we show that NATS
significantly outperforms existing methods such as information-greedy policies
or exhaustive search. We demonstrate the real-world viability of NATS using a
pseudo-realistic environment created in the Unreal Engine 4 game development
platform with the AirSim plugin.
Related papers
- SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy [55.570183323356964]
We propose a novel Surrounding-Aware Network, namely SurANet, for concealed object detection.
We enhance the semantics of feature maps using differential fusion of surrounding features to highlight concealed objects.
Next, a Surrounding-Aware Contrastive Loss is applied to identify the concealed object via learning surrounding feature maps contrastively.
arXiv Detail & Related papers (2024-10-09T13:02:50Z) - Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise.
We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations.
BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
arXiv Detail & Related papers (2024-07-10T18:00:54Z) - Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue
with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems.
This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
arXiv Detail & Related papers (2023-08-14T16:48:57Z) - Latent Exploration for Reinforcement Learning [87.42776741119653]
In Reinforcement Learning, agents learn policies by exploring and interacting with the environment.
We propose LATent TIme-Correlated Exploration (Lattice), a method to inject temporally-correlated noise into the latent state of the policy network.
arXiv Detail & Related papers (2023-05-31T17:40:43Z) - Virtual Reality via Object Poses and Active Learning: Realizing
Telepresence Robots with Aerial Manipulation Capabilities [39.29763956979895]
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
The proposed system not only features a haptic device, but also a virtual reality (VR) interface that provides real-time 3D displays of the robot's workspace.
We show over 70 robust executions of pick-and-place, force application and peg-in-hole tasks with the DLR cable-Suspended Aerial Manipulator (SAM)
arXiv Detail & Related papers (2022-10-18T08:42:30Z) - Multi-Agent Active Search using Detection and Location Uncertainty [6.587280549237275]
Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty.
We first propose an inference method to jointly handle both target detection and location uncertainty.
We then build a decision making algorithm that uses Thompson sampling to enable decentralized multi-agent active search.
arXiv Detail & Related papers (2022-03-09T04:53:37Z) - Towards Optimal Correlational Object Search [25.355936023640506]
Correlational Object Search POMDP can be solved to produce search strategies that use correlational information.
We conduct experiments using AI2-THOR, a realistic simulator of household environments, as well as YOLOv5, a widely-used object detector.
arXiv Detail & Related papers (2021-10-19T14:03:43Z) - Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping [19.43152908750153]
Practical applications occur in many real-world settings where robots need to interact with an unknown environment.
We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning.
We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.
arXiv Detail & Related papers (2021-03-09T12:51:17Z) - Batch Exploration with Examples for Scalable Robotic Reinforcement
Learning [63.552788688544254]
Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states.
BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
arXiv Detail & Related papers (2020-10-22T17:49:25Z) - Asynchronous Multi Agent Active Search [6.587280549237275]
We propose two distinct active search algorithms called SPATS (Sparse Parallel Asynchronous Thompson Sampling) and LATSI (LAplace Thompson Sampling with Information gain)
We consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions.
We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.
arXiv Detail & Related papers (2020-06-25T22:17:20Z) - AutoOD: Automated Outlier Detection via Curiosity-guided Search and
Self-imitation Learning [72.99415402575886]
Outlier detection is an important data mining task with numerous practical applications.
We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model.
Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
arXiv Detail & Related papers (2020-06-19T18:57:51Z)
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.