Team IHMC’s Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble

Johnson, Matthew; Shrewsbury, Brandon; Bertrand, Sylvain; Calvert, Duncan; Wu, Tingfan; Duran, Daniel; Stephen, Douglas; Mertins, Nathan; Carff, John; Rifenburgh, William; Smith, Jesper; Schmidt-Wetekam, Chris; Faconti, Davide; Graber-Tilton, Alex; Eyssette, Nicolas; Meier, T.; Kalkov, Igor; Craig, Travis; Payton, Nick; McCrory, Stephen; Wiedebach, Georg; Layton, Brooke; Neuhaus, Peter; Pratt, Jerry

doi:10.1007/978-3-319-74666-1_3

Cited by 22 publications

(33 citation statements)

References 13 publications

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“…There is a body of research on teamwork and information sharing in disaster response, with and without robots, e.g., (Casper and Murphy;Johnson et al, 2017;Toups et al, 2016;Carver and Turoff, 2007). There has been very little work on dialogue processing in this domain so far.…”

Section: Related Workmentioning

confidence: 99%

Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Anikina¹,

Kruijff-Korbayová

2019

Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response. We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embeddings, context and attention mechanism. The best performance was achieved with a "Divide & Merge" architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history. This model learns from the current utterance and the preceding context separately and then combines the two generated representations. Average accuracy of 10fold cross-validation is 79.8%, F-score 71.8%.

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Section: Related Workmentioning

confidence: 99%

Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Anikina¹,

Kruijff-Korbayová

2019

Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

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“…However, biped locomotion remains an important challenge, as illustrated during the terrain task of this contest. Indeed, during the corresponding trials, only 2 of the 16 teams successfully completed the entire terrain task without requiring an intervention, so that the walking challenge for the finals had to be simplified (Johnson et al, 2016). The present contribution does not target DRC-like tasks, but rather studies the scientific question of exploring the benefits of human-like musculoskeletal systems, together with their control properties.…”

Section: Perspectivesmentioning

confidence: 99%

Bio-inspired controller achieving forward speed modulation with a 3D bipedal walker

Noot

Ijspeert

Ronsse

2018

The International Journal of Robotics Research

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“…x (2). Both models require a COP controller that is able to track the desired COP, which is compatible with modern torque controlled hardware.…”

Section: Proposed Formulationmentioning

confidence: 99%

“…The performances achieved by legged robots during the DARPA Robotics Challenge (DRC) suggest that more improvements in control shall still be made [1]. The common approach for locomotion among all high ranked DRC humanoids is to split motion planning and stabilizing control into two separate parts [2], [3]. First, an optimal walking pattern under the given environmental constraint is found and used as reference in a feed-forward manner.…”

Section: Introductionmentioning

confidence: 99%

An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

Yuan

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Predictive control methods for walking commonly use low dimensional models, such as a Linear Inverted Pendulum Model (LIPM), for simplifying the complex dynamics of legged robots. This paper identifies the physical limitations of the modeling methods that do not account for external disturbances, and then analyzes the issues of numerical stability of Model Predictive Control (MPC) using different models with variable receding horizons. We propose a new modeling formulation that can be used for both gait planning and feedback control in an MPC scheme. The advantages are the improved numerical stability for long prediction horizons and the robustness against various disturbances. Benchmarks were rigorously studied to compare the proposed MPC scheme with the existing ones in terms of numerical stability and disturbance rejection. The effectiveness of the controller is demonstrated in both MATLAB and Gazebo simulations.

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Team IHMC’s Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble

Cited by 22 publications

References 13 publications

Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

Bio-inspired controller achieving forward speed modulation with a 3D bipedal walker

An Improved Formulation for Model Predictive Control of Legged Robots for Gait Planning and Feedback Control

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