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Results show that blended shared control leads to higher autonomous performance compared to typical methods for fine-tuning robot policies from fully teleoperated interventions.
This work proposes using erroneous examples to teach robot policies, extending an existing policy teaching framework and conducts a user study in which participants view incorrect demonstrations of robot behavior and correct the actions to align with the actual policy.
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