Robots That ‘Feel’ Their Way Through Tasks

Author: Denis Avetisyan

A new framework helps robots better understand and react to forces during complex manipulation, even with limited or noisy sensor data.

The system evolves beyond simple noise reduction, extending from scalar control of denoising to a time and modality-varying noise matrix, enabling a singular framework capable of generating diverse functionalities—from predictive models to sensitive anomaly detection—and demonstrating an adaptive capacity against inevitable systemic decay.

Researchers introduce Multimodal Diffusion Forcing, a method utilizing a time-modality noise level matrix to improve trajectory modeling and force reasoning for robust robot manipulation and anomaly detection.

While conventional imitation learning often overlooks the complex interplay between sensory inputs, actions, and rewards crucial for robust robot behavior, this work introduces ‘Unified Multimodal Diffusion Forcing for Forceful Manipulation’, a novel framework for modeling multimodal robot trajectories. By leveraging a time-modality noise level matrix and training a diffusion model to reconstruct masked trajectories, MDF learns temporal and cross-modal dependencies—enabling improved force reasoning and performance in contact-rich manipulation tasks. Our approach demonstrates strong results in both simulated and real-world environments, even with noisy or incomplete observations. Could this unified approach unlock new capabilities for anomaly detection and more adaptable robotic systems?

## The Inevitable Drift: Robotic Generalization and Its Limits

Traditional robotic systems struggle to generalize learned skills to novel tasks and environments, often requiring extensive retraining even with minor variations. This inflexibility stems from an over-reliance on pre-programmed behaviors or task-specific learning. Current methodologies, limited by hand-engineered features and narrow datasets, hinder a robot’s ability to adapt and maintain robust performance. The lack of generalization remains a significant obstacle to real-world robotic deployment.

The model learns to compress point clouds into compact embeddings via a diffusion-based autoencoder and processes six modalities—partial point cloud, full point cloud, force, action, reward, and proprioception—corrupting data with noise to train a diffusion transformer that captures temporal and cross-modal dependencies.

Addressing these challenges requires learning methods that capture underlying dynamics. Effective solutions infer generalizable principles from limited data and seamlessly transfer knowledge. The ultimate goal is to create robots that learn how to learn, rather than simply memorizing procedures.

## Diffusion Models: Sculpting Probability in Robotic Motion

Initially successful in image generation, Diffusion Models now offer a powerful framework for learning complex robot trajectories. This approach contrasts with traditional methods reliant on hand-engineered features or simplified dynamics. The inherent probabilistic nature captures nuanced movement patterns and effectively handles uncertainty in real-world tasks.

Methods like DDPM and Unified World Models extend this capability, allowing robots to learn from limited data and generalize. These models learn a diffusion process transforming data into noise, then reversing it to generate trajectories. This allows the robot to predict future states, even with incomplete or noisy observations.

When used as a dynamics model on the Nut Thread task, the model reconstructs partial (orange) and full (blue) point clouds, demonstrating its ability to predict future states.

3D Diffusion Policy further refines this approach by leveraging 3D visual representations, achieving improved performance and robustness in complex environments.

## Forging Coherence: Multimodal Diffusion Forcing

Multimodal Diffusion Forcing (MDF) establishes a unified framework for learning the joint distribution of multimodal robot trajectories. This approach moves beyond single-modality representations by explicitly modeling correlations between sensory inputs and robot actions. The innovation lies in its ability to synthesize coherent and diverse behaviors across various tasks.

A key component is the Time-Modality Noise Level Matrix, which precisely controls the noise level applied to each modality at every timestep, prioritizing specific inputs based on task requirements. Trajectory representation is achieved through a Latent Diffusion Transformer, facilitating efficient learning and sampling.

Evaluations demonstrate MDF’s superior performance. In the Nut Thread Task, the model achieves 100% success, exceeding DP3’s 96%. Similar improvements are observed in the Gear Mesh and Peg Insert Tasks, with a 26% performance increase on real-world car maintenance tasks.

The model's history length can be dynamically adjusted during testing to meet the specific requirements of the task at hand. — The model’s history length can be dynamically adjusted during testing to meet the specific requirements of the task at hand.

## Anticipating the Inevitable: Robustness Through Anomaly Detection

The Multimodal Diffusion Forcing (MDF) framework facilitates anomaly detection by identifying deviations from learned distributions of robot trajectories. This probabilistic approach models expected behavior and flags instances outside established norms, comparing observed states with a reconstructed expectation.

Within MDF, a Point Cloud Autoencoder reconstructs expected states, providing a baseline for anomaly identification. Evaluations across manipulation tasks, including the Oil Cap Installation and Removal Tasks, demonstrate significant improvements in robustness – a 23% and 70% performance increase over DP3 with corrupted data.

Evaluation of the model on the Oil Cap task demonstrates its effectiveness.

Furthermore, MDF exhibits superior anomaly localization accuracy across both wrench and point cloud modalities. This capacity to anticipate potential failures signifies an advancement in deploying robots within complex environments; a system that doesn’t simply react to failure, but anticipates its arrival.

The presented framework, Multimodal Diffusion Forcing, inherently acknowledges the inevitable entropy of robotic systems operating within complex environments. Just as structures age and require adaptation, the MDF model actively accounts for noise and partial observability, preventing brittle failure in the face of imperfect data. Barbara Liskov aptly stated, “Architecture without history is fragile and ephemeral.” This holds true; the time-modality noise level matrix functions as a ‘history’ of the system’s state, allowing the model to gracefully degrade rather than catastrophically fail when confronted with the realities of unpredictable force reasoning and trajectory modeling. The system doesn’t strive for an impossible perfection, but rather builds resilience through understanding the constraints of time and imperfect observation.

What’s Next?

The introduction of Multimodal Diffusion Forcing presents, predictably, not a resolution, but a refinement of the question. Every failure is a signal from time; the framework’s ability to navigate partial observability simply delays the inevitable erosion of predictive capacity. The time-modality noise level matrix, while elegant, addresses symptom, not cause. Future iterations will likely concern themselves with the inherent limitations of diffusion models when confronted with genuinely novel states – those outside the training manifold. The true challenge isn’t modeling trajectories, but acknowledging the impossibility of complete modeling.

A natural progression lies in exploring the interplay between data-driven diffusion and symbolic reasoning. The current approach excels at interpolation, but falters at extrapolation. Integrating prior knowledge, even imperfectly, could offer a path toward more robust, albeit less “fluid,” manipulation. Refactoring is a dialogue with the past; the system must learn not just how things have moved, but why they might deviate from those patterns.

Ultimately, the field will be defined not by the sophistication of its models, but by the humility with which it accepts uncertainty. Anomaly detection, after all, is merely a formalized acknowledgement of the system’s inherent blindness. The pursuit of perfect trajectory modeling is a fool’s errand; a graceful decay, a system that anticipates its own limitations, is the more attainable, and perhaps more valuable, goal.

Original article: https://arxiv.org/pdf/2511.04812.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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