# SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

arXiv: https://arxiv.org/abs/2501.14400 · HTML: https://mlsanity.com/paper/2501.14400/

## Key facts

- Cited by 21 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 63 of its 71 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## 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, 30 of 63)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [3D Diffuser Actor: Policy Diffusion with 3D Scene Representations](https://mlsanity.com/paper/2402.10885/) (2024)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Any-point Trajectory Modeling for Policy Learning](https://mlsanity.com/paper/2401.00025/) (2023)
- [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024)
- [Masked World Models for Visual Control](https://mlsanity.com/paper/2206.14244/) (2022)
- [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://mlsanity.com/paper/2312.06709/) (2023)
- [kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation](https://mlsanity.com/paper/1903.06684/) (2019)

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

- [PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation](https://mlsanity.com/paper/2601.03782/) (2026)
- [Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow](https://mlsanity.com/paper/2512.24766/) (2025)
- [MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models](https://mlsanity.com/paper/2606.13515/) (2026)
- [Action Tokenizer Matters in In-Context Imitation Learning](https://mlsanity.com/paper/2503.01206/) (2025)
- [Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulation](https://mlsanity.com/paper/2602.14193/) (2026)
- [From World Models to World Action Models: A Concise Tutorial for Robotics](https://mlsanity.com/paper/2607.00836/) (2026)
- [AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation](https://mlsanity.com/paper/2510.01433/) (2025)
- [Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping](https://mlsanity.com/paper/2603.03278/) (2026)
- [On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning](https://mlsanity.com/paper/2605.26649/) (2026)
- [SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding](https://mlsanity.com/paper/2512.12842/) (2025)
- [A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics](https://mlsanity.com/paper/2604.15215/) (2026)
- [Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation](https://mlsanity.com/paper/2606.23090/) (2026)
- [GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning](https://mlsanity.com/paper/2607.21049/) (2026)
- [ImaginationPolicy: Towards Generalizable, Precise and Reliable End-to-End Policy for Robotic Manipulation](https://mlsanity.com/paper/2509.20841/) (2025)
- [GLUE: Global-Local Unified Encoding for Imitation Learning via Key-Patch Tracking](https://mlsanity.com/paper/2509.23220/) (2025)
- [Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning](https://mlsanity.com/paper/2602.10594/) (2026)
- [HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions](https://mlsanity.com/paper/2605.10201/) (2026)
- [GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks](https://mlsanity.com/paper/2605.15836/) (2026)
- [FocalPolicy: Frequency-Optimized Chunking and Locally Anchored Flow Matching for Coherent Visuomotor Policy](https://mlsanity.com/paper/2605.15944/) (2026)
- [Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera](https://mlsanity.com/paper/2606.14535/) (2026)
- [Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling](https://mlsanity.com/paper/2609.31207/) (2026)

## Abstract (arXiv)

Real-world tasks such as garment manipulation and table rearrangement demand robots to perform generalizable, highly precise, and long-horizon actions. Although imitation learning has proven to be an effective approach for teaching robots new skills, large amounts of expert demonstration data are still indispensible for these complex tasks, resulting in high sample complexity and costly data collection. To address this, we propose Semantic Keypoint Imitation Learning (SKIL), a framework which automatically obtains semantic keypoints with the help of vision foundation models, and forms the descriptor of semantic keypoints that enables efficient imitation learning of complex robotic tasks with significantly lower sample complexity. In real-world experiments, SKIL doubles the performance of baseline methods in tasks such as picking a cup or mouse, while demonstrating exceptional robustness to variations in objects, environmental changes, and distractors. For long-horizon tasks like hanging a towel on a rack where previous methods fail completely, SKIL achieves a mean success rate of 70% with as few as 30 demonstrations. Furthermore, SKIL naturally supports cross-embodiment learning due to its semantic keypoints abstraction. Our experiments demonstrate that even human videos bring considerable improvement to the learning performance. All these results demonstrate the great success of SKIL in achieving data-efficient generalizable robotic learning. Visualizations and code are available at: https://skil-robotics.github.io/SKIL-robotics/.