Traversability classification using unsupervised on-line visual learning for outdoor robot navigation

Kim, Dongshin; Sun, Jie; Oh, Sang Min; Rehg, James M.; Bobick, Aaron F.

doi:10.1109/robot.2006.1641763

Cited by 49 publications

(12 citation statements)

References 15 publications

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“…While we have argued in previous work (Wellhausen et al, 2019;Wellhausen et al, 2020) that purely geometric approaches are not sufficient for navigation in natural outdoor environments, approaches relying on semantic information exhibit the same issues as traditional geometric approaches. They, either implicitly through semantic segmentation of the environment (Rothrock et al, 2016;Bradley et al, 2015;Otsu et al, 2016;Valada et al, 2017), or explicitly (Kim et al, 2006;Barnes et al, 2017;Hirose et al, 2018) predict a traversability label. However, we can instead reinterpret traversability labels as foothold feasibility labels and use them to enhance geometric planning.…”

Section: Related Workmentioning

confidence: 99%

ArtPlanner: Robust Legged Robot Navigation in the Field

Wellhausen¹,

Hutter²

2023

Preprint

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Due to the highly complex environment present during the DARPA Subterranean Challenge, all six funded teams relied on legged robots as part of their robotic team. Their unique locomotion skills of being able to step over obstacles require special considerations for navigation planning. In this work, we present and examine ArtPlanner, the navigation planner used by team CERBERUS during the Finals. It is based on a sampling-based method that determines valid poses with a reachability abstraction and uses learned foothold scores to restrict areas considered safe for stepping. The resulting planning graph is assigned learned motion costs by a neural network trained in simulation to minimize traversal time and limit the risk of failure. Our method 1 achieves real-time performance with a bounded computation time. We present extensive experimental results gathered during the Finals event of the DARPA Subterranean Challenge, where this method contributed to team CERBERUS winning the competition. It powered navigation of four ANYmal quadrupeds for 90 minutes of autonomous operation without a single planning or locomotion failure.

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Section: Related Workmentioning

confidence: 99%

ArtPlanner: Robust Legged Robot Navigation in the Field

Wellhausen¹,

Hutter²

2023

Preprint

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“…Many approaches to affordance learning has been developed: the traversability affordance for instance, has been studied in different works [4,22]. Likewise, the grasp affordance is a recurrent topic and various approaches exist to learn it such as learning based on visual descriptors or raw image input [11,17].…”

Section: Affordances Learningmentioning

confidence: 99%

Hierarchical Affordance Discovery using Intrinsic Motivation

Manoury

Nguyen

Buche

2019

Proceedings of the 7th International Conference on Human-Agent Interaction

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To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.

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“…A great progress in the field of autonomous, perception-based, off-road navigation in robotic unmanned ground vehicles (UGV) was influenced by the DARPA Learning Applied to Ground Vehicles (LAGR) program [2], which ran from 2004 until 2008. The challenge evoked several solutions [3][4][5] with different approaches of robot sensing which can be divided into two groups as: 1) exteroceptive; 2) and proprioceptive sensing.…”

Section: Related Workmentioning

confidence: 99%

On Traversability Cost Evaluation From Proprioceptive Sensing for a Crawling Robot

Mrva

Stejskal

Faigl

2015

APP

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Traversability characteristics of the robot working environment are crucial in planning an efficient path for a robot operating in rough unstructured areas. In the literature, approaches to wheeled or tracked robots can be found, but a relatively little attention is given to walking multi-legged robots. Moreover, the existing approaches for terrain traversability assessment seem to be focused on gathering key features from a terrain model acquired from range data or camera image and only occasionally supplemented with proprioceptive sensing that expresses the interaction of the robot with the terrain. This paper addresses the problem of traversability cost evaluation based on proprioceptive sensing for a hexapod walking robot while optimizing different criteria. We present several methods of evaluating the robot-terrain interaction that can be used as a cost function for an assessment of the robot motion that can be utilized in high-level path-planning algorithms.

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Traversability classification using unsupervised on-line visual learning for outdoor robot navigation

Cited by 49 publications

References 15 publications

ArtPlanner: Robust Legged Robot Navigation in the Field

ArtPlanner: Robust Legged Robot Navigation in the Field

Hierarchical Affordance Discovery using Intrinsic Motivation

On Traversability Cost Evaluation From Proprioceptive Sensing for a Crawling Robot

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