Bi-Manual Articulated Robot Teleoperation using an External RGB-D Range Sensor

Rolley-Parnell, Emily-Jane; Kanoulas, Dimitrios; Laurenzi, Arturo; Delhaisse, Brian; Rozo, Leonel; Caldwell, Darwin G.; Tsagarakis, Nikos G.

doi:10.1109/icarcv.2018.8581174

Cited by 34 publications

(13 citation statements)

References 23 publications

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“…Their advantage is that the current understanding of physical systems is refined and works well on fully actuated robots. Most methods focus on WBC for quadrupeds [14], [15], [16], humanoids [17], [18], [19], [20], [21], animaloids [22], [23], [24], or mobile manipulators [17], [25]. Model Predictive Control methods are popular with works such as Minniti et al [26] showing success in WBC pose-tracking and interaction tasks.…”

Section: Related Workmentioning

confidence: 99%

Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping

Hadjivelichkov¹,

Vlachos²,

Kanoulas³

2021

Preprint

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Many robotic path planning problems are continuous, stochastic, and high-dimensional. The ability of a mobile manipulator to coordinate its base and manipulator in order to control its whole-body online is particularly challenging when self and environment collision avoidance is required. Reinforcement Learning techniques have the potential to solve such problems through their ability to generalise over environments. We study joint penalties and joint limits of a state-of-the-art mobile manipulator whole-body controller that uses LIDAR sensing for obstacle collision avoidance. We propose directions to improve the reinforcement learning method. Our agent achieves significantly higher success rates than the baseline in a goal-reaching environment and it can solve environments that require coordinated whole-body control which the baseline fails.

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Section: Related Workmentioning

confidence: 99%

Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping

Hadjivelichkov¹,

Vlachos²,

Kanoulas³

2021

Preprint

Self Cite

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“…In both cases the main problem is to have enough actuation power, few mechanical constraints and a balance system able to handle strong loads (in the order of several hundreds or thousands of Newtons). Centaur robots (Rolley-Parnell et al (2018)), which consist of a quadruped lower body and a humanoid upper body, have also recently been developed (see Fig. 2).…”

Section: Multi-legged Robotmentioning

confidence: 99%

“…We would like to thanks the authors of Agravante et al (2019) Rolley-Parnell et al (2018 and Murooka et al (2017) for providing the images in Fig. 2 and Fig.…”

Section: Acknowledgementsmentioning

confidence: 99%

Whole-Body Manipulation

Stasse

Righetti

2020

Encyclopedia of Robotics

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“…In particular, in the first set of experiments the robot detects rough surfaces of the upcoming terrain to modify its Center-of-Mass (CoM) position for stability purposes, while in the second set of experiments the robot plans paths around risky surfaces (of high roughness) if possible. CENTAURO [45] is a 42-DoF wheeled-legged robot of 93kg and 1.5m height. In this work, we have used a monocular RGB camera (PointGrey BlackFly) mounted on its head.…”

Section: Robotics Applicationsmentioning

confidence: 99%

Terrain Segmentation and Roughness Estimation using RGB Data: Path Planning Application on the CENTAURO Robot

Suryamurthy

Raghavan

Laurenzi

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

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Robots operating in real world environments require a high-level perceptual understanding of the chief physical properties of the terrain they are traversing. In unknown environments, roughness is one such important terrain property that could play a key role in devising robot control/planning strategies. In this paper, we present a fast method for predicting pixel-wise labels of terrain (stone, sand, road/sidewalk, wood, grass, metal) and roughness estimation, using a single RGBbased deep neural network. Real world RGB images are used to experimentally validate the presented approach. Furthermore, we demonstrate an application of our proposed method on the centaur-like wheeled-legged robot CENTAURO, by integrating it with a navigation planner that is capable of re-configuring the leg joints to modify the robot footprint polygon for stability purposes or for safe traversal among obstacles.

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Bi-Manual Articulated Robot Teleoperation using an External RGB-D Range Sensor

Cited by 34 publications

References 23 publications

Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping

Improved Reinforcement Learning Coordinated Control of a Mobile Manipulator using Joint Clamping

Whole-Body Manipulation

Terrain Segmentation and Roughness Estimation using RGB Data: Path Planning Application on the CENTAURO Robot

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