Compliant residual DAgger: Improving real-world contact-rich manipulation with human corrections
Overview
This research addresses challenges in Dataset Aggregation (DAgger) for contact-rich manipulation by introducing Compliant Residual DAgger (CR-DAgger). The system features a compliant intervention interface for gentle human corrections without interrupting robot execution and a residual policy formulation that incorporates force feedback. Experiments show it improves base policy success rates by 64% on four tasks (book flipping, belt assembly, cable routing, gear insertion) using minimal correction data, outperforming retraining-from-scratch and fine-tuning approaches.
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Reported quantity · Reported success rate: 64 percent · Basis not reported
“action corrections without interrupting the ongoing robot policy execution; and (2) a Compliant Residual Policy formulation that learns from human corrections while incorporating force feedback and force control. Our system significantly enhances performance on precise contact-rich manipulation tasks using minimal correction data, improving base policy success rates by 64% on four challenging tasks (book flipping, b”
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- International Journal of Robotics Research — Journal metadataSignalCompliant residual DAgger: Improving real-world contact-rich manipulation with human corrections
This research addresses challenges in Dataset Aggregation (DAgger) for contact-rich manipulation by introducing Compliant Residual DAgger (CR-DAgger). The system features a compliant intervention interface for gentle human corrections without interrupting robot execution and a residual policy formulation that incorporates force feedback. Experiments show it improves base policy success rates by 64% on four tasks (book flipping, belt assembly, cable routing, gear insertion) using minimal correction data, outperforming retraining-from-scratch and fine-tuning approaches.
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