# RMPflow: A Computational Graph for Automatic Motion Policy Generation

arXiv: https://arxiv.org/abs/1811.07049 · HTML: https://mlsanity.com/paper/1811.07049/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 21 of 21)

- [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023)
- [ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes](https://mlsanity.com/paper/2304.04321/) (2023)
- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://mlsanity.com/paper/2509.07953/) (2025)
- [Hierarchical Neural Dynamic Policies](https://mlsanity.com/paper/2107.05627/) (2021)
- [Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning](https://mlsanity.com/paper/2105.04962/) (2021)
- [RMP2: A Structured Composable Policy Class for Robot Learning](https://mlsanity.com/paper/2103.05922/) (2021)
- [Hierarchical Policy Blending as Inference for Reactive Robot Control](https://mlsanity.com/paper/2210.07890/) (2022)
- [Motion Reasoning for Goal-Based Imitation Learning](https://mlsanity.com/paper/1911.05864/) (2019)
- [LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization](https://mlsanity.com/paper/2401.17500/) (2024)
- [Hierarchical Policy Blending As Optimal Transport](https://mlsanity.com/paper/2212.01938/) (2022)
- [Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation](https://mlsanity.com/paper/2508.15663/) (2025)
- [Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower](https://mlsanity.com/paper/2204.02390/) (2022)
- [Learning Dynamical Systems Encoding Non-Linearity within Space Curvature](https://mlsanity.com/paper/2403.11948/) (2024)
- [ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning](https://mlsanity.com/paper/2608.19182/) (2026)
- [Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories](https://mlsanity.com/paper/2105.03019/) (2021)
- [Switch: Learning Agile Skills Switching for Humanoid Robots](https://mlsanity.com/paper/2604.14834/) (2026)
- [Nonprehensile Riemannian Motion Predictive Control](https://mlsanity.com/paper/2111.07986/) (2021)
- [Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments](https://mlsanity.com/paper/2305.10580/) (2023)
- [Safe and Optimal Variable Impedance Control via Certified Reinforcement Learning](https://mlsanity.com/paper/2511.16330/) (2025)
- [Planning Neural Dynamics with Lie Group Embedding through Supervised Projective Manifold Learning](https://mlsanity.com/paper/2605.26167/) (2026)
- [WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning](https://mlsanity.com/paper/2609.18685/) (2026)

## Abstract (arXiv)

We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion policies designed to parameterize non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task spaces. Given a set of RMPs designed for individual tasks, RMPflow can consistently combine these local policies to generate an expressive global policy, while simultaneously exploiting sparse structure for computational efficiency. We study the geometric properties of RMPflow and provide sufficient conditions for stability. Finally, we experimentally demonstrate that accounting for the geometry of task policies can simplify classically difficult problems, such as planning through clutter on high-DOF manipulation systems.