StaKe adds lightweight auxiliary heads for manipulation stage identification and next-gripper-transition keyframe prediction to VLA fine-tuning, reporting relative success rate gains of 14% in bimanual simulation and 56% on single-arm real-robot tasks.
Waypoint- based imitation learning for robotic manipulation
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MoRI dynamically mixes RL and IL experts with variance-based switching and IL regularization to reach 97.5% success in four real-world robotic tasks while cutting human intervention by 85.8%.
ESPADA uses semantic segmentation from VLMs and LLMs plus DTW to downsample non-critical segments in demonstrations, delivering about 2x faster robot execution in behavior cloning while maintaining task success rates.
citing papers explorer
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Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision
StaKe adds lightweight auxiliary heads for manipulation stage identification and next-gripper-transition keyframe prediction to VLA fine-tuning, reporting relative success rate gains of 14% in bimanual simulation and 56% on single-arm real-robot tasks.
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MoRI: Mixture of RL and IL Experts for Long-Horizon Manipulation Tasks
MoRI dynamically mixes RL and IL experts with variance-based switching and IL regularization to reach 97.5% success in four real-world robotic tasks while cutting human intervention by 85.8%.
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ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning
ESPADA uses semantic segmentation from VLMs and LLMs plus DTW to downsample non-critical segments in demonstrations, delivering about 2x faster robot execution in behavior cloning while maintaining task success rates.