# Flow Matching Imitation Learning for Multi-Support Manipulation

arXiv: https://arxiv.org/abs/2407.12381 · HTML: https://mlsanity.com/paper/2407.12381/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (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)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Building Normalizing Flows with Stochastic Interpolants](https://mlsanity.com/paper/2209.15571/) (2022)
- [Stochastic Interpolants: A Unifying Framework for Flows and Diffusions](https://mlsanity.com/paper/2303.08797/) (2023)
- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [Riemannian Flow Matching Policy for Robot Motion Learning](https://mlsanity.com/paper/2403.10672/) (2024)
- [AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies](https://mlsanity.com/paper/2402.04292/) (2024)
- [Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints](https://mlsanity.com/paper/2109.13145/) (2021)
- [Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration](https://mlsanity.com/paper/2308.02459/) (2023)
- [Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control](https://mlsanity.com/paper/2206.00528/) (2022)
- [Multi-Contact Motion Retargeting using Whole-body Optimization of Full Kinematics and Sequential Force Equilibrium](https://mlsanity.com/paper/2206.00542/) (2022)
- [Multi-Contact Whole-Body Force Control for Position-Controlled Robots](https://mlsanity.com/paper/2312.16465/) (2023)
- [Humanoid Control Under Interchangeable Fixed and Sliding Unilateral Contacts](https://mlsanity.com/paper/2103.02906/) (2021)
- [Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction](https://mlsanity.com/paper/2308.03479/) (2023)

## Built on by (most cited first, 29 of 29)

- [FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation](https://mlsanity.com/paper/2412.04987/) (2024)
- [Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching](https://mlsanity.com/paper/2409.07343/) (2024)
- [Affordance-based Robot Manipulation with Flow Matching](https://mlsanity.com/paper/2409.01083/) (2024)
- [Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies](https://mlsanity.com/paper/2503.08558/) (2025)
- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [VITA: Vision-to-Action Flow Matching Policy](https://mlsanity.com/paper/2507.13231/) (2025)
- [GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning](https://mlsanity.com/paper/2505.18763/) (2025)
- [Fast and Robust Visuomotor Riemannian Flow Matching Policy](https://mlsanity.com/paper/2412.10855/) (2024)
- [Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation](https://mlsanity.com/paper/2412.09265/) (2024)
- [FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency](https://mlsanity.com/paper/2506.08822/) (2025)
- [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://mlsanity.com/paper/2507.15833/) (2025)
- [X-IL: Exploring the Design Space of Imitation Learning Policies](https://mlsanity.com/paper/2502.12330/) (2025)
- [VFP: Variational Flow-Matching Policy for Multi-Modal Robot Manipulation](https://mlsanity.com/paper/2508.01622/) (2025)
- [One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies](https://mlsanity.com/paper/2603.12480/) (2026)
- [From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model](https://mlsanity.com/paper/2605.22671/) (2026)
- [Local Flow Matching Generative Models](https://mlsanity.com/paper/2410.02548/) (2024)
- [UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning](https://mlsanity.com/paper/2506.02955/) (2025)
- [Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning](https://mlsanity.com/paper/2505.19717/) (2025)
- [Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges](https://mlsanity.com/paper/2508.17449/) (2025)
- [GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios](https://mlsanity.com/paper/2606.06967/) (2026)
- [Dense-Jump Flow Matching with Non-Uniform Time Scheduling for Robotic Policies: Mitigating Multi-Step Inference Degradation](https://mlsanity.com/paper/2509.13574/) (2025)
- [PRISM: Performer RS-IMLE for Single-pass Multisensory Imitation Learning](https://mlsanity.com/paper/2602.02396/) (2026)
- [Words2Contact: Identifying Support Contacts from Verbal Instructions Using Foundation Models](https://mlsanity.com/paper/2407.14229/) (2024)
- [Decision Flow Policy Optimization](https://mlsanity.com/paper/2505.20350/) (2025)
- [Towards Safe Imitation Learning via Potential Field-Guided Flow Matching](https://mlsanity.com/paper/2508.08707/) (2025)
- [Native Extrapolation Awareness in Flow-Based Conditional Generation](https://mlsanity.com/paper/2602.13061/) (2026)
- [Visual Prompt Guided Unified Pushing Policy](https://mlsanity.com/paper/2602.19193/) (2026)
- [Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning](https://mlsanity.com/paper/2605.08511/) (2026)
- [DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation](https://mlsanity.com/paper/2609.37348/) (2026)

## Abstract (arXiv)

Humanoid robots could benefit from using their upper bodies for support contacts, enhancing their workspace, stability, and ability to perform contact-rich and pushing tasks. In this paper, we propose a unified approach that combines an optimization-based multi-contact whole-body controller with Flow Matching, a recently introduced method capable of generating multi-modal trajectory distributions for imitation learning. In simulation, we show that Flow Matching is more appropriate for robotics than Diffusion and traditional behavior cloning. On a real full-size humanoid robot (Talos), we demonstrate that our approach can learn a whole-body non-prehensile box-pushing task and that the robot can close dishwasher drawers by adding contacts with its free hand when needed for balance. We also introduce a shared autonomy mode for assisted teleoperation, providing automatic contact placement for tasks not covered in the demonstrations. Full experimental videos are available at: https://hucebot.github.io/flow_multisupport_website/