# Keypoint Abstraction using Large Models for Object-Relative Imitation Learning

arXiv: https://arxiv.org/abs/2410.23254 · HTML: https://mlsanity.com/paper/2410.23254/

## Key facts

- Cited by 20 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 37 of its 56 references that are in the map.
- First posted on arXiv 30 Oct 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, 30 of 37)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://mlsanity.com/paper/2209.05451/) (2022)
- [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [3D Diffuser Actor: Policy Diffusion with 3D Scene Representations](https://mlsanity.com/paper/2402.10885/) (2024)
- [Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation](https://mlsanity.com/paper/2112.05124/) (2021)
- [Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning](https://mlsanity.com/paper/2311.17842/) (2023)
- [kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation](https://mlsanity.com/paper/1903.06684/) (2019)
- [Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation](https://mlsanity.com/paper/2306.17817/) (2023)
- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022)
- [Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation](https://mlsanity.com/paper/2308.07931/) (2023)
- [MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting](https://mlsanity.com/paper/2403.03174/) (2024)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [Entity Abstraction in Visual Model-Based Reinforcement Learning](https://mlsanity.com/paper/1910.12827/) (2019)
- [You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration](https://mlsanity.com/paper/2201.12716/) (2022)
- [KETO: Learning Keypoint Representations for Tool Manipulation](https://mlsanity.com/paper/1910.11977/) (2019)
- [Deep Object-Centric Representations for Generalizable Robot Learning](https://mlsanity.com/paper/1708.04225/) (2017)
- [SORNet: Spatial Object-Centric Representations for Sequential Manipulation](https://mlsanity.com/paper/2109.03891/) (2021)
- [kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation](https://mlsanity.com/paper/2102.06279/) (2021)
- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://mlsanity.com/paper/2103.00375/) (2021)
- [DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability](https://mlsanity.com/paper/2306.13196/) (2023)

## Built on by (most cited first, 20 of 20)

- [SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation](https://mlsanity.com/paper/2501.14400/) (2025)
- [AMPLIFY: Actionless Motion Priors for Robot Learning from Videos](https://mlsanity.com/paper/2506.14198/) (2025)
- [Robotic VLA Benefits from Joint Learning with Motion Image Diffusion](https://mlsanity.com/paper/2512.18007/) (2025)
- [AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation](https://mlsanity.com/paper/2510.01433/) (2025)
- [Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions](https://mlsanity.com/paper/2506.21057/) (2025)
- [Generalizable Hierarchical Skill Learning via Object-Centric Representation](https://mlsanity.com/paper/2510.21121/) (2025)
- [Tether: Autonomous Functional Play with Correspondence-Driven Trajectory Warping](https://mlsanity.com/paper/2603.03278/) (2026)
- [VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models](https://mlsanity.com/paper/2605.29562/) (2026)
- [ImitDiff: Transferring Foundation-Model Priors for Distraction Robust Visuomotor Policy](https://mlsanity.com/paper/2502.09649/) (2025)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [MSG: Multi-Stream Generative Policies for Sample-Efficient Robotic Manipulation](https://mlsanity.com/paper/2509.24956/) (2025)
- [Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks](https://mlsanity.com/paper/2509.24972/) (2025)
- [ManiSkillFormer: Demonstration-Free Compositional Manipulation via Geometric Contracts and Agentic Skill Graph](https://mlsanity.com/paper/2609.16331/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [GLUE: Global-Local Unified Encoding for Imitation Learning via Key-Patch Tracking](https://mlsanity.com/paper/2509.23220/) (2025)
- [ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation](https://mlsanity.com/paper/2512.16302/) (2025)
- [Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera](https://mlsanity.com/paper/2606.14535/) (2026)
- [GROW2: Grounding Which and Where for Robot Tool Use](https://mlsanity.com/paper/2606.30632/) (2026)
- [Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly](https://mlsanity.com/paper/2609.17714/) (2026)
- [Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations](https://mlsanity.com/paper/2610.17802/) (2026)

## Abstract (arXiv)

Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been proven effective as a succinct representation for capturing essential object features, and for establishing a reference frame in action prediction, enabling data-efficient learning of robot skills. However, their manual design nature and reliance on additional human labels limit their scalability. In this paper, we propose KALM, a framework that leverages large pre-trained vision-language models (LMs) to automatically generate task-relevant and cross-instance consistent keypoints. KALM distills robust and consistent keypoints across views and objects by generating proposals using LMs and verifies them against a small set of robot demonstration data. Based on the generated keypoints, we can train keypoint-conditioned policy models that predict actions in keypoint-centric frames, enabling robots to generalize effectively across varying object poses, camera views, and object instances with similar functional shapes. Our method demonstrates strong performance in the real world, adapting to different tasks and environments from only a handful of demonstrations while requiring no additional labels. Website: https://kalm-il.github.io/