# Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control

arXiv: https://arxiv.org/abs/2503.11801 · HTML: https://mlsanity.com/paper/2503.11801/

## Key facts

- Cited by 28 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 33 of its 43 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## 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, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion](https://mlsanity.com/paper/2407.01392/) (2024)
- [Is Conditional Generative Modeling all you need for Decision-Making?](https://mlsanity.com/paper/2211.15657/) (2022)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models](https://mlsanity.com/paper/2308.01557/) (2023)
- [Universal Humanoid Motion Representations for Physics-Based Control](https://mlsanity.com/paper/2310.04582/) (2023)
- [One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation](https://mlsanity.com/paper/2410.21257/) (2024)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations](https://mlsanity.com/paper/2310.10198/) (2023)
- [Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models](https://mlsanity.com/paper/2406.04806/) (2024)
- [Interactive Character Control with Auto-Regressive Motion Diffusion Models](https://mlsanity.com/paper/2306.00416/) (2023)
- [TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis](https://mlsanity.com/paper/2307.15042/) (2023)
- [DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets](https://mlsanity.com/paper/2404.19264/) (2024)
- [Hierarchical Planning and Control for Box Loco-Manipulation](https://mlsanity.com/paper/2306.09532/) (2023)
- [Neural Categorical Priors for Physics-Based Character Control](https://mlsanity.com/paper/2308.07200/) (2023)
- [Diffusion Predictive Control with Constraints](https://mlsanity.com/paper/2412.09342/) (2024)
- [PDP: Physics-Based Character Animation via Diffusion Policy](https://mlsanity.com/paper/2406.00960/) (2024)
- [ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model](https://mlsanity.com/paper/2410.07296/) (2024)
- [Morph: A Motion-free Physics Optimization Framework for Human Motion Generation](https://mlsanity.com/paper/2411.14951/) (2024)
- [AAMDM: Accelerated Auto-regressive Motion Diffusion Model](https://mlsanity.com/paper/2401.06146/) (2023)

## Built on by (most cited first, 28 of 28)

- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [Flow Policy Gradients for Robot Control](https://mlsanity.com/paper/2602.02481/) (2026)
- [SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control](https://mlsanity.com/paper/2512.03028/) (2025)
- [InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2602.06035/) (2026)
- [Towards Adaptable Humanoid Control via Adaptive Motion Tracking](https://mlsanity.com/paper/2510.14454/) (2025)
- [SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control](https://mlsanity.com/paper/2511.19236/) (2025)
- [ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation](https://mlsanity.com/paper/2607.08741/) (2026)
- [InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs](https://mlsanity.com/paper/2512.07410/) (2025)
- [TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size](https://mlsanity.com/paper/2603.07988/) (2026)
- [Gen-C: Populating Virtual Worlds with Generative Crowds](https://mlsanity.com/paper/2504.01924/) (2025)
- [Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making](https://mlsanity.com/paper/2512.08280/) (2025)
- [Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control](https://mlsanity.com/paper/2603.17834/) (2026)
- [Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking](https://mlsanity.com/paper/2606.26855/) (2026)
- [GPC: Large-Scale Generative Pretraining for Transferable Motor Control](https://mlsanity.com/paper/2606.29148/) (2026)
- [Scaling Behavior Foundation Model for Humanoid Robots](https://mlsanity.com/paper/2607.15163/) (2026)
- [Flexible Locomotion Learning with Diffusion Model Predictive Control](https://mlsanity.com/paper/2510.04234/) (2025)
- [X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models](https://mlsanity.com/paper/2605.25044/) (2026)
- [MIND: Multi-Scale Intent Diffusion for Text-Driven Physics-Based Humanoid Control](https://mlsanity.com/paper/2605.26006/) (2026)
- [DiscoForcing: A Unified Framework for Real-Time Audio-Driven Character Control with Diffusion Forcing](https://mlsanity.com/paper/2605.28491/) (2026)
- [PegasusFlow: Parallel Rolling-Denoising Score Sampling for Robot Diffusion Planner Flow Matching](https://mlsanity.com/paper/2509.08435/) (2025)
- [Robo-Saber: Generating and Simulating Virtual Reality Players](https://mlsanity.com/paper/2602.18319/) (2026)
- [SCDP: Learning Humanoid Locomotion from Partial Observations via Mixed-Observation Distillation](https://mlsanity.com/paper/2603.09574/) (2026)
- [NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control](https://mlsanity.com/paper/2605.20209/) (2026)
- [Recovering Physically Plausible Human-Object Interactions from Monocular Videos](https://mlsanity.com/paper/2606.05359/) (2026)
- [EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal](https://mlsanity.com/paper/2607.13472/) (2026)
- [CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration](https://mlsanity.com/paper/2609.06623/) (2026)
- [PredActor: Predictive Action Diffusion for Steerable Onboard Humanoid Control](https://mlsanity.com/paper/2609.24840/) (2026)

## Abstract (arXiv)

We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While existing kinematics motion generation with diffusion models offer intuitive steering capabilities with inference-time conditioning, they often fail to produce physically viable motions. In contrast, recent diffusion-based control policies have shown promise in generating physically realizable motion sequences, but the lack of kinematics prediction limits their steerability. Diffuse-CLoC addresses these challenges through a key insight: modeling the joint distribution of states and actions within a single diffusion model makes action generation steerable by conditioning it on the predicted states. This approach allows us to leverage established conditioning techniques from kinematic motion generation while producing physically realistic motions. As a result, we achieve planning capabilities without the need for a high-level planner. Our method handles a diverse set of unseen long-horizon downstream tasks through a single pre-trained model, including static and dynamic obstacle avoidance, motion in-betweening, and task-space control. Experimental results show that our method significantly outperforms the traditional hierarchical framework of high-level motion diffusion and low-level tracking.