WM-DAgger uses world models with corrective action synthesis and consistency-guided filtering to aggregate OOD recovery data for imitation learning, reporting 93.3% success in soft bag pushing with five demonstrations.
Hg-dagger: Interactive imitation learning with human experts,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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cs.RO 3years
2026 3representative citing papers
TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.
Adaptor uses few-shot learning with trajectory perturbation and vision-language conditioning to achieve robust cross-operator intent recognition and higher success rates in assistive teleoperation.
citing papers explorer
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WM-DAgger: Enabling Efficient Data Aggregation for Imitation Learning with World Models
WM-DAgger uses world models with corrective action synthesis and consistency-guided filtering to aggregate OOD recovery data for imitation learning, reporting 93.3% success in soft bag pushing with five demonstrations.
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Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.
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Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
Adaptor uses few-shot learning with trajectory perturbation and vision-language conditioning to achieve robust cross-operator intent recognition and higher success rates in assistive teleoperation.