mlsanityData to 14 Oct 2026

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

arXiv 2408.04587 ↗First posted 8 Aug 2024cs.ROcited by 23

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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/.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 12.

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  1. 2025ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulationcited by 91
  2. 2025Learning Dexterous Manipulation Skills from Imperfect Simulationscited by 9
  3. 2025Flow-based Domain Randomization for Learning and Sequencing Robotic Skillscited by 4
  4. 2025Multimodal Diffusion Forcing for Forceful Manipulationcited by 4
  5. 2025Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Taskscited by 3
  6. 2025Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Datacited by 3
  7. 2025DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulationcited by 2
  8. 2026Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Themcited by 2
  9. 2025AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Usecited by 1
  10. 2026CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptationcited by 1
  11. 2026FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Modelcited by 1
  12. 2026Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertioncited by 1
11 more
  1. 2024Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Taskscited by 0
  2. 2025EasyInsert: A Data-Efficient and Generalizable Insertion Policycited by 0
  3. 2025Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrationscited by 0
  4. 2026Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertaintycited by 0
  5. 2026SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policycited by 0
  6. 2026VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulationcited by 0
  7. 2026SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routingcited by 0
  8. 2026Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assemblycited by 0
  9. 2026From Documented Strengths to Force Limits: Material-Informed Robotic Insertion for Construction Assemblycited by 0
  10. 2026Generalizable Robotic Insertion with World Modelscited by 0
  11. 2026ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Modelscited by 0