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About this paper
Edwin Zhang, Yujie Lu, Shinda Huang, William Wang, Amy Zhang
The Language Control Diffusion (LCD) framework enables efficient and scalable language-conditioned robotic control from high-dimensional pixel inputs, by performing high-level planning in a low-dimensional latent space and using temporal abstraction. This approach achieves superior multi-task success rates on challenging benchmarks while demonstrating 3.3x to 15x faster inference speeds compared to prior diffusion-based methods.
Key insights
- Performing high-level planning in a compact, task-relevant latent space, derived from a goal-conditioned deep reinforcement learning model's encoder, is critical for efficiency and precision in high-dimensional control.
- Temporal abstraction through state subsampling combined with DDIM inference provides substantial computational speedups (3.3x to 15x faster inference) while maintaining strong performance.
- Diffusion models are superior to simpler MLP or Transformer architectures for modeling the goal space and generating coherent long-range plans in hierarchical control.
Summary by alphaXiv.
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