APPL: Adaptive Planner Parameter Learning

Xiao, Xuesu; Wang, Zizhao; Xu, Zifan; Liu, Bo; Warnell, Garrett; Dhamankar, Gauraang; Nair, Anirudh; Stone, Peter

doi:10.1016/j.robot.2022.104132

Cited by 21 publications

(9 citation statements)

References 16 publications

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“…• Digital twin models in Cyber-Physical Manufacturing Systems (CPMS) [73], [74] • Analog Twin (AT) Framework for Human and AI Supervisory Control [75] • Experimentation in a remote laboratory setting [76] • Multi-robot localization and mapping [23], [77], [78] • Learning from observation [25] • Mobile manipulator positioning for object pick-up [26] • Navigation and obstacle avoidance [35], [36], [79]- [85] • Autonomous exploration in indoor environments [30], [86]- [88] • Immersive telepresence [89] • The specific application was not mentioned [27]- [29], [32], [53], [90]-[95] Healthcare…”

Section: Researchmentioning

confidence: 99%

“…AMARL 29 [162], VFH+ 30 [76], SND 31 [76], A-FGM 32 [85], CNN [163], [164] 4 -Navigation CNN + MGRL 33 [165], RL + APPL 34 [53], DWA [166], BDI 35 [79], [123], ANN 36 + DPP 37 [24], B-spline curves [82], Petri Nets [111], PID 38 controller + RANSAC 39 [117], DRL [37], PaCcET [72], AMCL 41 [167]- [172], Control Mechanisms 1 -Motion Control RBFNN 42 [115], ANN [173], Fuzzy inference [174], DDPG [51], Kalman Filters [86], B-RV [131], TD3 44 [26], Evolutionary algorithm [35]…”

Section: Householdmentioning

confidence: 99%

“…PeopleBot [42], [91] Pioneer 3-DX [19], [36], [42], [72], [119] Clearpath Robotics Husky [28], [75], [85], [116], [174], [175], [178] Clearpath Robotics Jackal [18], [28], [53], [94] Willow Garage TurtleBot series [2], [8], [9], [12], [25], [30], [35], [70], [81], [88], [100], [105], [143], [144], [147], [148], [150], [158], [178]- [185] Willow Garage PR2 [87], [124] Roomba vacuum cleaner [86], [172] Eddie [186] Rob@work 3 [187] KUKA YouBot [138], [142] Arlobot [134] PlatypOUs [137] Ceres (Volksbot platform)…”

Section: Mobile Robotsmentioning

confidence: 99%

“…Reference Numbers Gazebo Simulators Gazebo only [13], [15], [18], [22], [28], [30], [33], [35], [36], [53], [78], [84]- [88], [90], [92]- [96], [98], [102], [104]- [107], [111], [112], [115], [116], [123], [125], [131], [137], [140], [143], [144], [147], [150], [151], [155], [156], [158], [159], [162], [166], [178], [181], [188], [192], [193], [195], [197], [199]-[202] Gazebo + RViz [1],…”

Section: Simulator Typementioning

confidence: 99%

“…Robots can quickly adapt to changing situations, correct errors, and perform better when given direct human input. Over time, the robot refines its behaviour to the point where human intervention becomes minimal [53]. This synergy between hardware, software, and human expertise accelerates the transition from simulations to real-world applications.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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