研究実績の概要 |
In FY2020, we succeeded at showing that humans learning a real or a virtual tooling task with force feedback exhibited the same learning patterns judged on a variety of metrics (peak force, arm's stiffness, tool velocity). Our results showed that skilled users could move and insert the tool in a single ballistic motion, suggesting that human motion planning optimizes the tool's motion and contact phases together, unlike most state-of-the-art algorithms which plan and control each phase separately. Based on these observations and insights from our earlier studies in this project, we have developed a robotic controller robust to feedback delays and sudden changes in the environment, in preparation for publication.
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