# AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries

arXiv: https://arxiv.org/abs/2407.08028 · HTML: https://mlsanity.com/paper/2407.08028/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2024.

## Built on by (most cited first, 26 of 26)

- [LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulation](https://mlsanity.com/paper/2505.11528/) (2025)
- [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024)
- [Local Policies Enable Zero-shot Long-horizon Manipulation](https://mlsanity.com/paper/2410.22332/) (2024)
- [PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation](https://mlsanity.com/paper/2504.16693/) (2025)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning](https://mlsanity.com/paper/2510.14930/) (2025)
- [Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects](https://mlsanity.com/paper/2502.07005/) (2025)
- [Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learning](https://mlsanity.com/paper/2603.15789/) (2026)
- [Neural Robot Dynamics](https://mlsanity.com/paper/2508.15755/) (2025)
- [Agent as Policy for Robotic Manipulation](https://mlsanity.com/paper/2609.12541/) (2026)
- [DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter](https://mlsanity.com/paper/2602.05513/) (2026)
- [Zero-Shot Visual Generalization in Robot Manipulation](https://mlsanity.com/paper/2505.11719/) (2025)
- [DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation](https://mlsanity.com/paper/2510.05662/) (2025)
- [AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects](https://mlsanity.com/paper/2605.12845/) (2026)
- [ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors](https://mlsanity.com/paper/2603.15956/) (2026)
- [Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?](https://mlsanity.com/paper/2606.26428/) (2026)
- [Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion](https://mlsanity.com/paper/2609.13516/) (2026)
- [A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks](https://mlsanity.com/paper/2504.14820/) (2025)
- [EasyInsert: A Data-Efficient and Generalizable Insertion Policy](https://mlsanity.com/paper/2505.16187/) (2025)
- [Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation](https://mlsanity.com/paper/2510.26656/) (2025)
- [Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch](https://mlsanity.com/paper/2604.09100/) (2026)
- [VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation](https://mlsanity.com/paper/2605.29564/) (2026)
- [Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly](https://mlsanity.com/paper/2609.07534/) (2026)
- [Generalizable Robotic Insertion with World Models](https://mlsanity.com/paper/2609.28258/) (2026)
- [AlphaNIST: Emergent Assembly Skills from Binary Task Rewards](https://mlsanity.com/paper/2610.13354/) (2026)
- [ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models](https://mlsanity.com/paper/2610.18596/) (2026)

## Abstract (arXiv)

Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-to-real have led to tremendous progress. Inspired by such work, we present AutoMate, a learning framework and system that consists of 4 parts: 1) a dataset of 100 assemblies compatible with simulation and the real world, along with parallelized simulation environments for policy learning, 2) a novel simulation-based approach for learning specialist (i.e., part-specific) policies and generalist (i.e., unified) assembly policies, 3) demonstrations of specialist policies that individually solve 80 assemblies with 80% or higher success rates in simulation, as well as a generalist policy that jointly solves 20 assemblies with an 80%+ success rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better) performance than simulation, including on perception-initialized assembly. The key methodological takeaway is that a union of diverse algorithms from manufacturing engineering, character animation, and time-series analysis provides a generic and robust solution for a diverse range of robotic assembly problems. To our knowledge, AutoMate provides the first simulation-based framework for learning specialist and generalist policies over a wide range of assemblies, as well as the first system demonstrating zero-shot sim-to-real transfer over such a range. For videos and additional details, please see our project website: https://bingjietang718.github.io/automate/