# ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds

arXiv: https://arxiv.org/abs/2211.09006 · HTML: https://mlsanity.com/paper/2211.09006/

## Key facts

- Cited by 54 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.
- First posted on arXiv 16 Nov 2022.

## Placed in

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

## Built on by (most cited first, 30 of 54)

- [RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation](https://mlsanity.com/paper/2311.01455/) (2023)
- [Any-point Trajectory Modeling for Policy Learning](https://mlsanity.com/paper/2401.00025/) (2023)
- [Flow as the Cross-Domain Manipulation Interface](https://mlsanity.com/paper/2407.15208/) (2024)
- [Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation](https://mlsanity.com/paper/2405.01527/) (2024)
- [General Flow as Foundation Affordance for Scalable Robot Learning](https://mlsanity.com/paper/2401.11439/) (2024)
- [PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation](https://mlsanity.com/paper/2601.03782/) (2026)
- [Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy](https://mlsanity.com/paper/2403.06467/) (2024)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation](https://mlsanity.com/paper/2309.15596/) (2023)
- [RoboGround: Robotic Manipulation with Grounded Vision-Language Priors](https://mlsanity.com/paper/2504.21530/) (2025)
- [PoCo: Policy Composition from and for Heterogeneous Robot Learning](https://mlsanity.com/paper/2402.02511/) (2024)
- [FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation](https://mlsanity.com/paper/2303.02346/) (2023)
- [HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation](https://mlsanity.com/paper/2305.03942/) (2023)
- [Pre-training Auto-regressive Robotic Models with 4D Representations](https://mlsanity.com/paper/2502.13142/) (2025)
- [Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow](https://mlsanity.com/paper/2512.24766/) (2025)
- [Real-World Offline Reinforcement Learning from Vision Language Model Feedback](https://mlsanity.com/paper/2411.05273/) (2024)
- [SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation](https://mlsanity.com/paper/2501.14400/) (2025)
- [Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning](https://mlsanity.com/paper/2405.18196/) (2024)
- [Grasp Multiple Objects with One Hand](https://mlsanity.com/paper/2310.15599/) (2023)
- [Composable Part-Based Manipulation](https://mlsanity.com/paper/2405.05876/) (2024)
- [TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos](https://mlsanity.com/paper/2511.21690/) (2025)
- [GMSF: Global Matching Scene Flow](https://mlsanity.com/paper/2305.17432/) (2023)
- [Object-centric 3D Motion Field for Robot Learning from Human Videos](https://mlsanity.com/paper/2506.04227/) (2025)
- [Robot Fleet Learning via Policy Merging](https://mlsanity.com/paper/2310.01362/) (2023)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [MILES: Making Imitation Learning Easy with Self-Supervision](https://mlsanity.com/paper/2410.19693/) (2024)
- [H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos](https://mlsanity.com/paper/2512.09406/) (2025)
- [Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation](https://mlsanity.com/paper/2412.02676/) (2024)
- [Pri4R: Learning World Dynamics for Vision-Language-Action Models with Privileged 4D Representation](https://mlsanity.com/paper/2603.01549/) (2026)
- [Learning Generalizable Tool-use Skills through Trajectory Generation](https://mlsanity.com/paper/2310.00156/) (2023)

## Abstract (arXiv)

Point clouds are a widely available and canonical data modality which convey the 3D geometry of a scene. Despite significant progress in classification and segmentation from point clouds, policy learning from such a modality remains challenging, and most prior works in imitation learning focus on learning policies from images or state information. In this paper, we propose a novel framework for learning policies from point clouds for robotic manipulation with tools. We use a novel neural network, ToolFlowNet, which predicts dense per-point flow on the tool that the robot controls, and then uses the flow to derive the transformation that the robot should execute. We apply this framework to imitation learning of challenging deformable object manipulation tasks with continuous movement of tools, including scooping and pouring, and demonstrate significantly improved performance over baselines which do not use flow. We perform 50 physical scooping experiments with ToolFlowNet and attain 82% scooping success. See https://tinyurl.com/toolflownet for supplementary material.