German Research Center for Artificial Intelligence | Motion Capture State Feedback for Real-Time Control of a Humanoid Robot @dfkivideo | Uploaded 1 year ago | Updated 16 hours ago
The Video illustrates the results of the paper
Mihaela Popescu, Dennis Mronga, Ivan Bergonzani, Shivesh Kumar, Frank Kirchner: "Experimental Investigations into Using Motion Capture State Feedback for Real-Time Control of a Humanoid Robot", Accepted for Publication: MDPI Sensors Journal, Special Issue "Advanced Sensors Technologies Applied in Mobile Robot", 2022.
The work proposes the use of motion capture systems for stabilization and Whole-Body Control of a humanoid robot. The external motion capture state feedback for robot control is compared to a proprioceptive state estimation feedback, that relies on inertial measurement unit, joint encoders, and foot contact sensors. The framework is evaluated on the humanoid robot RH5 performing two sets of motions, namely squatting and single leg balancing.
The first section of the video demonstrates the execution of squats on the humanoid robot RH5 using state feedback from the external motion capture system and proprioceptive state estimation, respectively. The results show that the squatting movements are more stable and present less oscillations when using the proposed external motion capture framework for state feedback.
The second part of the video demonstrates the execution of single leg balancing movements on the humanoid robot RH5, by raising the left foot at 15 cm height. Again, both state feedback methods are employed, namely motion capture and proprioceptive state estimation. The robot center of mass (CoM) presents better stability and tracking of the desired trajectory when using the motion capture system.
Overall, the performance of the proposed method based on motion capture state feedback shows better results than proprioceptive state estimation for Whole-Body Control in terms of stability and motion tracking. In future, motion capture systems could be used both in industrial workspaces such as factories and in research laboratories in parallel with the development of better proprioceptive state estimation approaches to improve Whole-Body Control algorithms and explore the capabilities of humanoid robots.
Paper: mdpi.com/1424-8220/22/24/9853
More information:
Systems
RH5: https://robotik.dfki-bremen.de/en/research/robot-systems/rh5/
RH5 Manus: https://robotik.dfki-bremen.de/en/research/robot-systems/rh5-manus/
Software
https://robotik.dfki-bremen.de/en/research/softwaretools/arc-opt/
https://robotik.dfki-bremen.de/en/research/softwaretools/hyrodyn/
Projects
M-RoCK: https://robotik.dfki-bremen.de/en/research/projects/m-rock/
VeryHuman: https://robotik.dfki-bremen.de/en/research/projects/veryhuman/
Acknowledgments:
This work has been performed in the M-RoCK (FKZ 01IW21002) and VeryHuman (FKZ 01IW20004) projects funded by the German Aerospace Center (DLR) with federal funds from the Federal Ministry of Education and Research (BMBF) and is additionally supported with project funds from the federal state of Bremen for setting up the Underactuated Robotics Lab (Grant Number: 201-001-10-3/2021-3-2).
The Video illustrates the results of the paper
Mihaela Popescu, Dennis Mronga, Ivan Bergonzani, Shivesh Kumar, Frank Kirchner: "Experimental Investigations into Using Motion Capture State Feedback for Real-Time Control of a Humanoid Robot", Accepted for Publication: MDPI Sensors Journal, Special Issue "Advanced Sensors Technologies Applied in Mobile Robot", 2022.
The work proposes the use of motion capture systems for stabilization and Whole-Body Control of a humanoid robot. The external motion capture state feedback for robot control is compared to a proprioceptive state estimation feedback, that relies on inertial measurement unit, joint encoders, and foot contact sensors. The framework is evaluated on the humanoid robot RH5 performing two sets of motions, namely squatting and single leg balancing.
The first section of the video demonstrates the execution of squats on the humanoid robot RH5 using state feedback from the external motion capture system and proprioceptive state estimation, respectively. The results show that the squatting movements are more stable and present less oscillations when using the proposed external motion capture framework for state feedback.
The second part of the video demonstrates the execution of single leg balancing movements on the humanoid robot RH5, by raising the left foot at 15 cm height. Again, both state feedback methods are employed, namely motion capture and proprioceptive state estimation. The robot center of mass (CoM) presents better stability and tracking of the desired trajectory when using the motion capture system.
Overall, the performance of the proposed method based on motion capture state feedback shows better results than proprioceptive state estimation for Whole-Body Control in terms of stability and motion tracking. In future, motion capture systems could be used both in industrial workspaces such as factories and in research laboratories in parallel with the development of better proprioceptive state estimation approaches to improve Whole-Body Control algorithms and explore the capabilities of humanoid robots.
Paper: mdpi.com/1424-8220/22/24/9853
More information:
Systems
RH5: https://robotik.dfki-bremen.de/en/research/robot-systems/rh5/
RH5 Manus: https://robotik.dfki-bremen.de/en/research/robot-systems/rh5-manus/
Software
https://robotik.dfki-bremen.de/en/research/softwaretools/arc-opt/
https://robotik.dfki-bremen.de/en/research/softwaretools/hyrodyn/
Projects
M-RoCK: https://robotik.dfki-bremen.de/en/research/projects/m-rock/
VeryHuman: https://robotik.dfki-bremen.de/en/research/projects/veryhuman/
Acknowledgments:
This work has been performed in the M-RoCK (FKZ 01IW21002) and VeryHuman (FKZ 01IW20004) projects funded by the German Aerospace Center (DLR) with federal funds from the Federal Ministry of Education and Research (BMBF) and is additionally supported with project funds from the federal state of Bremen for setting up the Underactuated Robotics Lab (Grant Number: 201-001-10-3/2021-3-2).
