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One promising approach towards effective robot decision making in complex, long-horizon tasks is to sequence together parameterized skills. We consider a setting where a robot is initially equipped with (1) a library of parameterized skills, (2) an AI planner for sequencing together the skills given a goal, and (3) a very general prior distribution for selecting skill parameters. Once deployed, the robot should rapidly and autonomously learn to improve its performance by specializing its skill parameter selection policy to the particular objects, goals, and constraints in its environment. In this work, we focus on the active learning problem of choosing which skills to practice to maximize expected future task success. We propose that the robot should estimate the competence of each skill, extrapolate the competence (asking: “how much would the competence improve through practice?”), and situate the skill in the task distribution through competence- aware planning. This approach is implemented within a fully autonomous system where the robot repeatedly plans, practices, and learns without any environment resets. Through experiments in simulation, we find that our approach learns effective pa- rameter policies more sample-efficiently than several baselines. Experiments in the real-world demonstrate our approach’s ability to handle noise from perception and control and improve the robot’s ability to solve two long-horizon mobile-manipulation tasks after a few hours of autonomous practice. Project website: http://ees.csail.mit.edu
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Continuous Gesture Control of a Robot Arm: Performance Is Robust to a Variety of Hand-to-Robot Maps
Objective: Despite advances in human-machine interface design, we lack the ability to give people precise and fast control over high degree of freedom (DOF) systems, like robotic limbs. Attempts to improve control often focus on the static map that links user input to device commands; hypothesizing that the user’s skill acquisition can be improved by finding an intuitive map. Here we investigate what map features affect skill acquisition. Methods: Each of our 36 participants used one of three maps that translated their 19-dimensional finger movement into the 5 robot joints and used the robot to pick up and move objects. The maps were each constructed to maximize a different control principle to reveal what features are most critical for user performance. 1) Principal Components Analysis to maximize the linear capture of finger variance, 2) our novel Egalitarian Principal Components Analysis to maximize the equality of variance captured by each component and 3) a Nonlinear Autoencoder to achieve both high variance capture and less biased variance allocation across latent dimensions Results: Despite large differences in the mapping structures there were no significant differences in group performance. Conclusion: Participants’ natural aptitude had a far greater effect on performance than the map. Significance: Robot-user interfaces are becoming increasingly common and require new designs to make them easier to operate. Here we show that optimizing the map may not be the appropriate target to improve operator skill. Therefore, further efforts should focus on other aspects of the robot-user-interface such as feedback or learning environment.
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- Award ID(s):
- 2430423
- PAR ID:
- 10553046
- Publisher / Repository:
- IEEE Xplore
- Date Published:
- Journal Name:
- IEEE Transactions on Biomedical Engineering
- Volume:
- 71
- Issue:
- 3
- ISSN:
- 0018-9294
- Page Range / eLocation ID:
- 944 to 953
- Subject(s) / Keyword(s):
- Assistive robotic manipulator human-machine interface machine learning teleoperation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TBME.2023.3323601
