HiPolicy is a new hierarchical multi-frequency action chunking method for imitation learning that jointly generates coarse and fine action sequences with entropy-guided execution to improve performance and efficiency in robotic manipulation.
Demospeedup: Accelerating visuomotor policies via entropy-guided demonstration acceleration
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AutoSpeed learns annotation-free, stage-adaptive robot motion speeds by optimizing policies toward the minimum-cost DCT-retimed multi-speed demonstration target.
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.
TSD applies two physics metrics to identify salient trajectory segments for dataset compression and expansion in robotic imitation learning, yielding comparable performance with 25% less data on average.
A single vision-language-action policy can execute robot manipulation at commanded speeds from 0.5x to 2x by training on merged/split demonstration actions conditioned on a speed scalar.
citing papers explorer
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HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
HiPolicy is a new hierarchical multi-frequency action chunking method for imitation learning that jointly generates coarse and fine action sequences with entropy-guided execution to improve performance and efficiency in robotic manipulation.
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AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation
AutoSpeed learns annotation-free, stage-adaptive robot motion speeds by optimizing policies toward the minimum-cost DCT-retimed multi-speed demonstration target.
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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.
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TSD: A Physics-Inspired Trajectory Saliency Detector for Efficient Imitation Learning
TSD applies two physics metrics to identify salient trajectory segments for dataset compression and expansion in robotic imitation learning, yielding comparable performance with 25% less data on average.
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TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
A single vision-language-action policy can execute robot manipulation at commanded speeds from 0.5x to 2x by training on merged/split demonstration actions conditioned on a speed scalar.