# What are the current mainstream and emerging policy architectures in robot imitation learning and VLA, and what are their limitations?

> Source: <https://discuss.huggingface.co/t/what-are-the-current-mainstream-and-emerging-policy-architectures-in-robot-imitation-learning-and-vla-and-what-are-their-limitations/191715#post_2>
> Published: 2026-11-11 21:14:44+00:00

Welcome!  Short version, based on recent surveys and papers:

**1. Mainstream**

- Chunked policies trained by imitation: ACT-style transformers and Diffusion Policy.
- VLAs: a pretrained VLM backbone plus an action head. Two families: autoregressive action tokens (RT-2, OpenVLA) and diffusion/flow-matching heads (π0, π0.5, GR00T, SmolVLA). Dual-system designs (a slow VLM planner plus a fast low-level policy) are common too. See this [survey](https://arxiv.org/abs/2509.19012) .

**2. Limitations**

- **Data:** VLAs need a lot of demos, and performance drops under distribution shift.
- **Physical properties:** vision doesn’t show mass, friction or stiffness, so a vision-only VLA is[blind to contact forces](https://arxiv.org/pdf/2606.29089) .
- **Latency:** big models are slow, so actions can be computed from stale observations.
- **Evaluation:** benchmarks like LIBERO are near saturated (>98%), so they say little about real generalization ([survey](https://www.preprints.org/manuscript/202604.0664) ).

**3. Emerging directions (matching your interests)**

- **Tactile/force VLAs** for weight, friction and stiffness:[Tactile-VLA](https://arxiv.org/abs/2507.09160) ,[TacVLA](https://arxiv.org/abs/2603.12665) , and[HapticVLA](https://arxiv.org/abs/2603.15257) , which distills force info so no force sensor is needed at inference.
- **Real-time execution:** Real-Time Chunking (asynchronous chunk generation, works with flow-based VLAs without retraining), and RL fine-tuning for real-time control ([Real-Time EXPO-FT](https://alphaxiv.org/abs/2609.18207) ).
- **RL post-training** on top of pretrained VLAs for reliability and data efficiency.

Tip: you also posted this in Research, so keeping one thread will help you get answers in one place.