Perspectives on Human-Robot Team Performance from an Evaluation of the DARPA Robotics Challenge

Norton, Adam; Ober, Willard; Baraniecki, Lisa; Shane, David; Skinner, Anna; Yanco, Holly A.

doi:10.1007/978-3-319-74666-1_16

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…In the wellknown DARPA Robotics Challenge (DRC) [4], for example, the participants had to tackle eight tasks to investigate the efficiency of supervised robotics in disaster response scenarios. The authors of [11] report that human-robot interaction had a considerable influence on the performance of the teams. The two top-ranked teams at DRC [12], [13] employed bipedal humanoid robots to handle difficult tasks in unstructured environments.…”

Section: Related Workmentioning

confidence: 99%

Affordance-Based Grasping and Manipulation in Real World Applications

Pohl

Hitzler

Grimm

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In real world applications, robotic solutions remain impractical due to the challenges that arise in unknown and unstructured environments. To perform complex manipulation tasks in complex and cluttered situations, robots need to be able to identify the interaction possibilities with the scene, i. e. the affordances of the objects encountered. In unstructured environments with noisy perception, insufficient scene understanding and limited prior knowledge, this is a challenging task. In this work, we present an approach for grasping unknown objects in cluttered scenes with a humanoid robot in the context of a nuclear decommissioning task. Our approach combines the convenience and reliability of autonomous robot control with the precision and adaptability of teleoperation in a semi-autonomous selection of grasp affordances. Additionally, this allows exploiting the expert knowledge of an experienced human worker. To evaluate our approach, we conducted 75 real world experiments with more than 660 grasp executions on the humanoid robot ARMAR-6. The results demonstrate that high-level decisions made by the human operator, supported by autonomous robot control, contribute significantly to successful task execution.

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Section: Related Workmentioning

confidence: 99%

Affordance-Based Grasping and Manipulation in Real World Applications

Pohl

Hitzler

Grimm

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Naturalistic Scene Modelling: Deep Learning with Insights from Biology

Appiah,

Jin,

Shi

et al. 2023

J Sign Process Syst

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Advances in machine learning coupled with the abundances of training data has facilitated the deep learning era, which has demonstrated its ability and effectiveness in solving complex detection and recognition problems. In general application areas with elements of machine learning have seen exponential growth with promising new and sophisticated solutions to complex learning problems. In computer vision, the challenge related to the detection of known objects in a scene is a thing of the past. With the tremendous increase in detection accuracies, some close to that of human detection, there are several areas still lagging in computer vision and machine learning where improvements may call for more architectural designs. In this paper, we propose a physiologically inspired model for scene understanding that encodes three key components: object location, size and category. Our aim is to develop an energy efficient artificial intelligent model for naturalistic scene understanding capable of deploying on a low power neuromorphic hardware. We have reviewed recent advances in deep learning architecture that have taken inspiration from human or primate learning systems and provided direct to future advancement on deep learning with inspiration from physiological experiments. Upon a review of areas that have benefitted from deep learning, we provide recommendations for enhancing those areas that might have stalled or grinded to a halt with little or no significant improvement.

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