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

2024-08-08

arXiv.org

AI Summary

This work addresses robotic assembly tasks requiring precise force application under pose uncertainty and frequent physical contact. We propose a force-aware sim-to-real transfer framework that introduces a novel force-guided exploration paradigm. Our method integrates a force-threshold-conditioned policy network, dynamics randomization during training, and a task-success probability prediction model to enable online, adaptive tuning of force constraints during deployment. Unlike conventional controllers relying on fixed gains and accurate pose estimates, our approach eliminates strict pose dependency and supports safe, multi-stage adaptive manipulation. We validate the framework on fully autonomous planetary gear assembly—including snap-fit insertion, nut threading, and gear meshing—under significant pose errors. Results demonstrate high task success rates and robust contact safety, even with substantial pose uncertainty. This work establishes a new paradigm for dexterous force-controlled assembly in unstructured, uncertain environments.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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/.

Problem

• Robotic Manipulation
• Uncertain Object Position
• Forceful Operation Robustness

Innovation

• FORGE Method
• Force-Limited Robotic Manipulation
• Adaptive Force Control