# FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty

arXiv: https://arxiv.org/abs/2408.04587 · HTML: https://mlsanity.com/paper/2408.04587/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2024.

## Built on by (most cited first, 23 of 23)

- [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025)
- [Learning Dexterous Manipulation Skills from Imperfect Simulations](https://mlsanity.com/paper/2512.02011/) (2025)
- [Flow-based Domain Randomization for Learning and Sequencing Robotic Skills](https://mlsanity.com/paper/2502.01800/) (2025)
- [Multimodal Diffusion Forcing for Forceful Manipulation](https://mlsanity.com/paper/2511.04812/) (2025)
- [Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2503.21989/) (2025)
- [Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data](https://mlsanity.com/paper/2510.02738/) (2025)
- [DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation](https://mlsanity.com/paper/2510.05662/) (2025)
- [Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them](https://mlsanity.com/paper/2608.01402/) (2026)
- [AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use](https://mlsanity.com/paper/2509.15153/) (2025)
- [CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation](https://mlsanity.com/paper/2605.22082/) (2026)
- [FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model](https://mlsanity.com/paper/2607.01212/) (2026)
- [Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion](https://mlsanity.com/paper/2609.13516/) (2026)
- [Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks](https://mlsanity.com/paper/2409.16208/) (2024)
- [EasyInsert: A Data-Efficient and Generalizable Insertion Policy](https://mlsanity.com/paper/2505.16187/) (2025)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)
- [Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty](https://mlsanity.com/paper/2604.19677/) (2026)
- [SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy](https://mlsanity.com/paper/2605.12247/) (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)
- [SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing](https://mlsanity.com/paper/2607.04616/) (2026)
- [Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly](https://mlsanity.com/paper/2609.07534/) (2026)
- [From Documented Strengths to Force Limits: Material-Informed Robotic Insertion for Construction Assembly](https://mlsanity.com/paper/2609.22609/) (2026)
- [Generalizable Robotic Insertion with World Models](https://mlsanity.com/paper/2609.28258/) (2026)
- [ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models](https://mlsanity.com/paper/2610.18596/) (2026)

## Abstract (arXiv)

We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, FORGE combines a force threshold mechanism with a dynamics randomization scheme to enable robust transfer of the learned policies to the real robot. At deployment, FORGE policies, conditioned on a maximum allowable force, adaptively perform contact-rich tasks while avoiding aggressive and unsafe behaviour, regardless of the controller gains. Additionally, FORGE policies predict task success, enabling efficient termination and autonomous tuning of the force threshold. We show that FORGE can be used to learn a variety of robust contact-rich policies, including the forceful insertion of snap-fit connectors. We further demonstrate the multistage assembly of a planetary gear system, which requires success across three assembly tasks: nut threading, insertion, and gear meshing. Project website can be accessed at https://noseworm.github.io/forge/.