TY - GEN
T1 - Know your limits! Optimize the robot's behavior through self-awareness
AU - Mascaró, Esteve Valls
AU - Lee, Dongheui
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - As humanoid robots transition from labs to realworld environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot's capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator's task. For that, we introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Then, our system can generate multiple references given a highlevel task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy.
AB - As humanoid robots transition from labs to realworld environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot's capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator's task. For that, we introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Then, our system can generate multiple references given a highlevel task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85214445993&partnerID=8YFLogxK
U2 - 10.1109/Humanoids58906.2024.10769929
DO - 10.1109/Humanoids58906.2024.10769929
M3 - Conference contribution
AN - SCOPUS:85214445993
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 258
EP - 265
BT - 2024 IEEE-RAS 23rd International Conference on Humanoid Robots, Humanoids 2024
PB - IEEE Computer Society
T2 - 23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024
Y2 - 22 November 2024 through 24 November 2024
ER -