AutoSERL achieves strong performance on six real-world robot manipulation tasks using RL guided by a single demonstration via sliding-window intervention, safety recovery, and automatic termination.
Thriftydagger: Budget-aware novelty and risk gating for interactive imitation learning
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.RO 7roles
background 2polarities
background 2representative citing papers
RePO-VLA raises average adversarial success rates in VLA manipulation from 20% to 75% by using recovery-aware initialization, a progress-aware semantic value function, and value-conditioned refinement on success and corrective trajectories.
A verifier called Future Forward Dynamics Causal Attention enables adaptive action execution in World Action Models, reducing model inferences by 69% and improving success rates in robotic tasks.
TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.
DeMaVLA is a VLA foundation model using a pruned action expert and flow matching, pre-trained on 5000 hours of real demonstrations and post-trained on multi-task folding data with human-in-the-loop correction, reporting competitive benchmark and real-world folding performance.
MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.
VR-DAgger is a VR-centered human-in-the-loop framework that applies MC dropout uncertainty to select and correct failure segments in diffusion policy rollouts, yielding up to 23 percentage point gains over behavioral cloning and 40% lower per-sample collection time on three dexterous tasks.
citing papers explorer
-
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.