# FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation arXiv: https://arxiv.org/abs/2607.28596 ยท HTML: https://mlsanity.com/paper/2607.28596/ ## Key facts - Cited by 3 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 30 of its 33 references that are in the map. - First posted on arXiv 30 Jul 2026. ## 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 30) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023) - [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025) - [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025) - [Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation](https://mlsanity.com/paper/2405.07503/) (2024) - [Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation](https://mlsanity.com/paper/2503.02881/) (2025) - [ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation](https://mlsanity.com/paper/2505.22159/) (2025) - [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024) - [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023) - [ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation](https://mlsanity.com/paper/2406.01586/) (2024) - [One-step Latent-free Image Generation with Pixel Mean Flows](https://mlsanity.com/paper/2601.22158/) (2026) - [AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing](https://mlsanity.com/paper/2606.09811/) (2026) - [MP1: MeanFlow Tames Policy Learning in 1-step for Robotic Manipulation](https://mlsanity.com/paper/2507.10543/) (2025) - [FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning](https://mlsanity.com/paper/2502.17432/) (2025) - [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025) - [DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2501.10356/) (2025) - [VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs](https://mlsanity.com/paper/2603.23481/) (2026) - [FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency](https://mlsanity.com/paper/2506.08822/) (2025) - [FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13102/) (2026) - [ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation](https://mlsanity.com/paper/2509.19047/) (2025) - [Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.22088/) (2026) - [ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning](https://mlsanity.com/paper/2512.10946/) (2025) - [Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies](https://mlsanity.com/paper/2606.03847/) (2026) - [HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning](https://mlsanity.com/paper/2604.06067/) (2026) - [Rethinking Camera Choice: An Empirical Study on Fisheye Camera Properties in Robotic Manipulation](https://mlsanity.com/paper/2603.02139/) (2026) - [Learning High-Frequency Continuous Action Chunks in Latent Space](https://mlsanity.com/paper/2605.24931/) (2026) - [FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning](https://mlsanity.com/paper/2606.12406/) (2026) - [Hybrid Consistency Policy: Decoupling Multi-Modal Diversity and Real-Time Efficiency in Robotic Manipulation](https://mlsanity.com/paper/2510.26670/) (2025) ## Built on by (most cited first, 3 of 3) - [ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.18242/) (2026) - [Toward Real-Time VLAs: Stage-Aware Two-Step Flow Denoising and System-Level Evaluation](https://mlsanity.com/paper/2609.39822/) (2026) - [Chronos Policy: Learning Robot Policies on Physical Time](https://mlsanity.com/paper/2610.13929/) (2026) ## Abstract (arXiv) In contact-rich manipulation, action multimodality and reactivity dominate different stages of a single episode. Before contact, multiple trajectories might be equally valid, making it important to preserve diverse action modes. After contact, geometric constraints and force limits narrow the solution space, while successful execution demands rapid responses to force feedback. However, standard diffusion policies use a fixed inference frequency and sampling steps throughout the episode, forcing a fundamental compromise: low-frequency, multi-step sampling better preserves pre-contact multimodality but responds slowly to force feedback, whereas high-frequency sampling improves reactivity but tends to collapse distinct pre-contact modes. To resolve this tradeoff, we present FA-RDP, a frequency-adaptive reactive diffusion policy. A shared multi-frequency visual-force Transformer predicts action chunks at both low and high frequencies, while a learned multimodality indicator dynamically selects multi-step low-frequency sampling before contact and one-step high-frequency sampling as action ambiguity decreases. We further introduce Manifold Consistency Distillation (MCD), which reparameterizes the diffusion network to predict actions on the robot action manifold while retaining DDPM-based residual supervision. Experiments on three contact-rich manipulation tasks show that FA-RDP achieves the highest success rate while preserving diverse pre-contact trajectory modes. Code and videos are available at https://fa-rdp.github.io.