Human-centered environments provide affordances for and require the use of two-handed, or bimanual, manipulations. Robots designed to function in, and physically interact with, these environments have not been able to meet these requirements because standard bimanual control approaches have not accommodated the diverse, dynamic, and intricate coordinations between two arms to complete bimanual tasks. In this work, we enabled robots to more effectively perform bimanual tasks by introducing a bimanual shared-control method. The control method moves the robot’s arms to mimic the operator’s arm movements but provides on-the-fly assistance to help the user complete tasks more easily. Our method used a bimanual action vocabulary, constructed by analyzing how people perform two-hand manipulations, as the core abstraction level for reasoning about how to assist in bimanual shared autonomy. The method inferred which individual action from the bimanual action vocabulary was occurring using a sequence-to-sequence recurrent neural network architecture and turned on a corresponding assistance mode, signals introduced into the shared-control loop designed to make the performance of a particular bimanual action easier or more efficient. We demonstrate the effectiveness of our method through two user studies that show that novice users could control a robot to complete a range of complex manipulation tasks more successfully using our method compared to alternative approaches. We discuss the implications of our findings for real-world robot control scenarios.
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Drosophibot: a fruit fly inspired bio-robot
We introduce Drosophibot, a hexapod robot with legs
designed based on the Common fruit fly, Drosophila melanogaster, built
as a test platform for neural control development. The robot models
anatomical aspects not present in other, similar bio-robots such as a
retractable abdominal segment, insect-like dynamic scaling, and compliant
feet segments in the hopes that more similar biomechanics will
lead to more similar neural control and resulting behaviors. In increasing
biomechanical modeling accuracy, we aim to gain further insight into
the insect’s nervous system to inform the current model and subsequent
neural controllers for legged robots.
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- Award ID(s):
- 1704436
- NSF-PAR ID:
- 10119320
- Date Published:
- Journal Name:
- 8th International Conference, Living Machines 2019
- Page Range / eLocation ID:
- 146-157
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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