Constrained Decoding for Robotics Foundation Models

2025-09-01

AI Summary

Robot foundation models often generate actions that violate behavioral correctness and safety constraints, compromising operational safety and logical consistency. Method: We propose the first constraint-decoding framework tailored for robot foundation models, employing Signal Temporal Logic (STL) as a formal specification language to perform real-time verification and correction of action sequences during decoding—without requiring model retraining. The framework supports multimodal inputs, conditional action generation, and dynamic intervention, and is compatible with mainstream navigation foundation models. Contribution/Results: Experiments demonstrate that our approach effectively filters unsafe actions, improves task success rates, and enhances cross-scenario generalization. To the best of our knowledge, this is the first work to enable plug-and-play integration of STL constraints into the inference pipeline of robot foundation models, thereby ensuring safety and logical fidelity in open-world robotic deployment.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Other Foundations and Applications
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic systems. These models are pretrained on vast datasets of robot trajectories to process multi- modal inputs and directly output a sequence of action that the system then executes in the real world. Although this approach is attractive from the perspective of im- proved generalization across diverse tasks, these models are still data-driven and, therefore, lack explicit notions of behavioral correctness and safety constraints. We address these limitations by introducing a constrained decoding framework for robotics foundation models that enforces logical constraints on action trajec- tories in dynamical systems. Our method ensures that generated actions provably satisfy signal temporal logic (STL) specifications at runtime without retraining, while remaining agnostic of the underlying foundation model. We perform com- prehensive evaluation of our approach across state-of-the-art navigation founda- tion models and we show that our decoding-time interventions are useful not only for filtering unsafe actions but also for conditional action-generation. Videos available on our website: https://constrained-robot-fms.github.io

Problem

• Enforcing safety constraints on robot action trajectories
• Ensuring actions satisfy signal temporal logic specifications
• Providing runtime safety without retraining foundation models

Innovation

• Constrained decoding enforces logical action constraints
• Ensures runtime STL specification satisfaction without retraining
• Agnostic framework works with various foundation models