# To the Noise and Back: Diffusion for Shared Autonomy

arXiv: https://arxiv.org/abs/2302.12244 · HTML: https://mlsanity.com/paper/2302.12244/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model](https://mlsanity.com/paper/2212.00490/) (2022)
- [Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning](https://mlsanity.com/paper/2208.06193/) (2022)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Imitating Human Behaviour with Diffusion Models](https://mlsanity.com/paper/2301.10677/) (2023)
- [Accelerating Diffusion Models via Early Stop of the Diffusion Process](https://mlsanity.com/paper/2205.12524/) (2022)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [Residual Policy Learning for Shared Autonomy](https://mlsanity.com/paper/2004.05097/) (2020)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization](https://mlsanity.com/paper/2205.12381/) (2022)
- [Highly Parallelized Data-driven MPC for Minimal Intervention Shared Control](https://mlsanity.com/paper/1906.02318/) (2019)
- [On Optimizing Interventions in Shared Autonomy](https://mlsanity.com/paper/2112.09169/) (2021)
- [Learning to Arbitrate Human and Robot Control using Disagreement between Sub-Policies](https://mlsanity.com/paper/2108.10634/) (2021)
- [Towards Automated Sample Collection and Return in Extreme Underwater Environments](https://mlsanity.com/paper/2112.15127/) (2021)

## Built on by (most cited first, 22 of 22)

- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets](https://mlsanity.com/paper/2404.19264/) (2024)
- [Inference-Time Policy Steering through Human Interactions](https://mlsanity.com/paper/2411.16627/) (2024)
- [GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy](https://mlsanity.com/paper/2410.17488/) (2024)
- [Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion](https://mlsanity.com/paper/2402.16075/) (2024)
- [Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition](https://mlsanity.com/paper/2407.00299/) (2024)
- [Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models](https://mlsanity.com/paper/2503.23271/) (2025)
- [APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Models](https://mlsanity.com/paper/2404.02284/) (2024)
- [Generative AI Agents in Autonomous Machines: A Safety Perspective](https://mlsanity.com/paper/2410.15489/) (2024)
- [Improving Robotic Generalist Policies via Flow Reversal Steering](https://mlsanity.com/paper/2606.13675/) (2026)
- [R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion prediction](https://mlsanity.com/paper/2306.09483/) (2023)
- [Autonomous Human-Robot Interaction via Operator Imitation](https://mlsanity.com/paper/2504.02724/) (2025)
- [6-DoF Stability Field via Diffusion Models](https://mlsanity.com/paper/2310.17649/) (2023)
- [FlashBack: Consistency Model-Accelerated Shared Autonomy](https://mlsanity.com/paper/2505.16892/) (2025)
- [Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models](https://mlsanity.com/paper/2506.14727/) (2025)
- [SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA](https://mlsanity.com/paper/2606.15568/) (2026)
- [Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies](https://mlsanity.com/paper/2406.13711/) (2024)
- [Diffusion-SAFE: Diffusion-Native Human-to-Robot Driving Handover for Shared Autonomy](https://mlsanity.com/paper/2505.09889/) (2025)
- [Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs](https://mlsanity.com/paper/2606.10180/) (2026)
- [Assistron: Bayesian Shared Autonomy with Off-the-shelf Vision-Language-Action Models](https://mlsanity.com/paper/2606.23147/) (2026)
- [Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning](https://mlsanity.com/paper/2609.30462/) (2026)
- [DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity](https://mlsanity.com/paper/2610.14372/) (2026)

## Abstract (arXiv)

Shared autonomy is an operational concept in which a user and an autonomous agent collaboratively control a robotic system. It provides a number of advantages over the extremes of full-teleoperation and full-autonomy in many settings. Traditional approaches to shared autonomy rely on knowledge of the environment dynamics, a discrete space of user goals that is known a priori, or knowledge of the user's policy -- assumptions that are unrealistic in many domains. Recent works relax some of these assumptions by formulating shared autonomy with model-free deep reinforcement learning (RL). In particular, they no longer need knowledge of the goal space (e.g., that the goals are discrete or constrained) or environment dynamics. However, they need knowledge of a task-specific reward function to train the policy. Unfortunately, such reward specification can be a difficult and brittle process. On top of that, the formulations inherently rely on human-in-the-loop training, and that necessitates them to prepare a policy that mimics users' behavior. In this paper, we present a new approach to shared autonomy that employs a modulation of the forward and reverse diffusion process of diffusion models. Our approach does not assume known environment dynamics or the space of user goals, and in contrast to previous work, it does not require any reward feedback, nor does it require access to the user's policy during training. Instead, our framework learns a distribution over a space of desired behaviors. It then employs a diffusion model to translate the user's actions to a sample from this distribution. Crucially, we show that it is possible to carry out this process in a manner that preserves the user's control authority. We evaluate our framework on a series of challenging continuous control tasks, and analyze its ability to effectively correct user actions while maintaining their autonomy.