UF-RNN: Real-Time Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction

2025-10-11

AI Summary

Robotic real-time motion generation often lacks robustness under state uncertainty—e.g., ambiguous object attributes or unpredictable interactions—leading to failure-prone behavior. Method: We propose UF-RNN, the first model integrating an uncertainty-driven foresight prediction module with a recurrent neural network (RNN). It leverages chaotic dynamics in latent space to autonomously induce exploratory behavior and enable proactive risk avoidance—even without failure-labeled training samples. The Foresight module recursively simulates multiple future trajectories and optimizes the hidden state by minimizing predictive variance, enhancing adaptive responsiveness to high-uncertainty scenarios. Results: Evaluated on simulated and real-robot door-opening tasks, UF-RNN significantly outperforms a stochastic RNN baseline in success rate, demonstrating superior effectiveness and generalization in complex, dynamic environments.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Training robots to operate effectively in environments with uncertain states, such as ambiguous object properties or unpredictable interactions, remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where uncertainty is most pronounced. To address this limitation, we propose the Uncertainty-driven Foresight Recurrent Neural Network (UF-RNN), a model that combines standard time-series prediction with an active "Foresight" module. This module performs internal simulations of multiple future trajectories and refines the hidden state to minimize predicted variance, enabling the model to selectively explore actions under high uncertainty. We evaluate UF-RNN on a door-opening task in both simulation and a real-robot setting, demonstrating that, despite the absence of explicit failure demonstrations, the model exhibits robust adaptation by leveraging self-induced chaotic dynamics in its latent space. When guided by the Foresight module, these chaotic properties stimulate exploratory behaviors precisely when the environment is ambiguous, yielding improved success rates compared to conventional stochastic RNN baselines. These findings suggest that integrating uncertainty-driven foresight into imitation learning pipelines can significantly enhance a robot's ability to handle unpredictable real-world conditions.

Problem

• Training robots for uncertain environments with ambiguous properties
• Addressing imitation learning's neglect of failure scenarios
• Enhancing robot adaptation in unpredictable real-world conditions

Innovation

• Combines time-series prediction with foresight module
• Performs internal simulations of multiple future trajectories
• Refines hidden state to minimize predicted variance