During loco-manipulation, instabilities to the robot's base can be introduced by the manipulator's motions. Trajectories that are generated on-the-fly may jeopardize the stability and safety of the robot and its surroundings. This work proposes a self-supervised learning-based pipeline to keep a robot stable while executing a given trajectory. Empirical results show that the desired objective can be achieved with the proposed pipeline. Experiments are done in simulation and on hardware on a unique multi-modal, manipulation-capable legged robot, and its scalability is tested on a conventional manipulator.
|Title of host publication||2022 19th International Conference on Ubiquitous Robots, UR 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2022|
|Event||19th International Conference on Ubiquitous Robots, UR 2022 - Jeju, Korea, Republic of|
Duration: 2022 Jul 4 → 2022 Jul 6
|Name||2022 19th International Conference on Ubiquitous Robots, UR 2022|
|Conference||19th International Conference on Ubiquitous Robots, UR 2022|
|Country/Territory||Korea, Republic of|
|Period||22/7/4 → 22/7/6|
Bibliographical noteFunding Information:
*This work was supported by the ONR through grant N00014-15-1-2064.
© 2022 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence
- Hardware and Architecture
- Mechanical Engineering
- Control and Optimization