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Dense Policy: Bidirectional Autoregressive Learning of Actions
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Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, have shown suboptimal results. This motivates a search for more effective learning methods to unleash the potential of autoregressive policies for robotic manipulation. This paper introduces a bidirectionally expanded learning approach, termed Dense Policy, to establish a new paradigm for autoregressive policies in action prediction. It employs a lightweight encoder-only architecture to iteratively unfold the action sequence from an initial single frame into the target sequence in a coarse-to-fine manner with logarithmic-time inference. Extensive experiments validate that our dense policy has superior autoregressive learning capabilities and can surpass existing holistic generative policies. Our policy, example data, and training code will be publicly available upon publication. Project page: https: //selen-suyue.github.io/DspNet/.
Forward citations
Cited by 4 Pith papers
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H$^3$DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
H3DP couples depth-layered, multi-scale visual features to coarse-to-fine denoising stages, reporting a +27.5% relative success-rate improvement over DP3 across 44 simulation tasks.
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Motion Before Action: Diffusing Object Motion as Manipulation Condition
MBA is a plug-in module that generates robot actions by first diffusing predicted object pose sequences, then conditioning action diffusion on those poses, improving imitation-learning success rates across simulation ...
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RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
A benchmark and modular policy show that explicit memory components substantially improve robotic manipulation on tasks requiring recall of past observations.
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OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation
OpenHelix shows that a frozen vision-language model with a prompt-tuned token and an auxiliary action-prediction head beats full fine-tuning on CALVIN language generalization while training far fewer parameters.
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