# Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control

arXiv: https://arxiv.org/abs/2410.09309 · HTML: https://mlsanity.com/paper/2410.09309/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2024.

## Built on by (most cited first, 30 of 41)

- [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)
- [FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning](https://mlsanity.com/paper/2502.17432/) (2025)
- [TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models](https://mlsanity.com/paper/2509.07962/) (2025)
- [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025)
- [FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2602.02142/) (2026)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings](https://mlsanity.com/paper/2505.01179/) (2025)
- [ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning](https://mlsanity.com/paper/2512.10946/) (2025)
- [SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space](https://mlsanity.com/paper/2507.01723/) (2025)
- [Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids](https://mlsanity.com/paper/2508.12252/) (2025)
- [FILIC: Dual-Loop Force-Guided Imitation Learning with Impedance Torque Control for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.17053/) (2025)
- [Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation](https://mlsanity.com/paper/2410.19235/) (2024)
- [CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation](https://mlsanity.com/paper/2512.14689/) (2025)
- [Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.19696/) (2025)
- [Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data](https://mlsanity.com/paper/2510.02738/) (2025)
- [Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback](https://mlsanity.com/paper/2606.14218/) (2026)
- [N0-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation](https://mlsanity.com/paper/2607.23783/) (2026)
- [ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System](https://mlsanity.com/paper/2411.09942/) (2024)
- [HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments](https://mlsanity.com/paper/2511.20275/) (2025)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [Enabling Dynamic Tracking in Vision-Language-Action Models via Time-Discrete and Time-Continuous Velocity Feedforward](https://mlsanity.com/paper/2603.16218/) (2026)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation](https://mlsanity.com/paper/2604.22551/) (2026)
- [TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models](https://mlsanity.com/paper/2606.29089/) (2026)
- [Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion](https://mlsanity.com/paper/2607.17257/) (2026)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)
- [FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation](https://mlsanity.com/paper/2609.03889/) (2026)
- [CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2609.05832/) (2026)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)

## Abstract (arXiv)

Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/