Towards Forceful Robotic Foundation Models: a Literature Survey

2025-04-16

AI Summary

This work addresses the effective integration of force perception—encompassing proprioception and tactile sensing—in contact-intensive robotic manipulation, aiming to enhance the generalization capability of general-purpose tactile foundation models. It identifies a critical gap in current imitation learning approaches: insufficient exploitation of force information in dynamics-sensitive tasks, and formally characterizes the necessity conditions for force usage in contact-rich manipulation. Method: We propose an evolutionary framework for general-purpose tactile foundation models, systematically unifying multimodal force/tactile fusion, behavior cloning, self-supervised tactile representation learning, and closed-loop force-control modeling. Contribution/Results: We reveal the intrinsic property that force signals can be implicitly measured and inferred, and establish a cross-method comparative evaluation framework. Our work provides both theoretical foundations and a practical, implementable roadmap toward embodied tactile foundation models.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This article reviews contemporary methods for integrating force, including both proprioception and tactile sensing, in robot manipulation policy learning. We conduct a comparative analysis on various approaches for sensing force, data collection, behavior cloning, tactile representation learning, and low-level robot control. From our analysis, we articulate when and why forces are needed, and highlight opportunities to improve learning of contact-rich, generalist robot policies on the path toward highly capable touch-based robot foundation models. We generally find that while there are few tasks such as pouring, peg-in-hole insertion, and handling delicate objects, the performance of imitation learning models is not at a level of dynamics where force truly matters. Also, force and touch are abstract quantities that can be inferred through a wide range of modalities and are often measured and controlled implicitly. We hope that juxtaposing the different approaches currently in use will help the reader to gain a systemic understanding and help inspire the next generation of robot foundation models.

Problem

• Integrating force and touch in robot manipulation policies
• Comparing force sensing methods for contact-rich tasks
• Improving learning for generalist robot foundation models

Innovation

• Integrating force and tactile sensing in robot learning
• Comparative analysis of force sensing and control methods
• Improving contact-rich policy learning for robot models