# RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches

arXiv: https://arxiv.org/abs/2403.02709 · HTML: https://mlsanity.com/paper/2403.02709/

## Key facts

- Cited by 41 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 49 references that are in the map.
- First posted on arXiv 5 Mar 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)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [VIMA: General Robot Manipulation with Multimodal Prompts](https://mlsanity.com/paper/2210.03094/) (2022)
- [Goal-Conditioned Imitation Learning using Score-based Diffusion Policies](https://mlsanity.com/paper/2304.02532/) (2023)
- [Interactive Language: Talking to Robots in Real Time](https://mlsanity.com/paper/2210.06407/) (2022)
- [Language Conditioned Imitation Learning over Unstructured Data](https://mlsanity.com/paper/2005.07648/) (2020)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [Language-Driven Representation Learning for Robotics](https://mlsanity.com/paper/2302.12766/) (2023)
- [Goal-conditioned Imitation Learning](https://mlsanity.com/paper/1906.05838/) (2019)
- [kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation](https://mlsanity.com/paper/1903.06684/) (2019)
- [RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches](https://mlsanity.com/paper/2311.01977/) (2023)
- [Data Quality in Imitation Learning](https://mlsanity.com/paper/2306.02437/) (2023)
- [Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?](https://mlsanity.com/paper/2204.11134/) (2022)
- [Photo-Sketching: Inferring Contour Drawings from Images](https://mlsanity.com/paper/1901.00542/) (2019)
- [CLIPascene: Scene Sketching with Different Types and Levels of Abstraction](https://mlsanity.com/paper/2211.17256/) (2022)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
- [Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly](https://mlsanity.com/paper/1910.13675/) (2019)
- [Picture that Sketch: Photorealistic Image Generation from Abstract Sketches](https://mlsanity.com/paper/2303.11162/) (2023)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)

## Built on by (most cited first, 30 of 41)

- [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://mlsanity.com/paper/2510.07077/) (2025)
- [Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2508.13073/) (2025)
- [RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation](https://mlsanity.com/paper/2407.04689/) (2024)
- [RoboGround: Robotic Manipulation with Grounded Vision-Language Priors](https://mlsanity.com/paper/2504.21530/) (2025)
- [Interleave-VLA: Enhancing Robot Manipulation with Interleaved Image-Text Instructions](https://mlsanity.com/paper/2505.02152/) (2025)
- [RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation](https://mlsanity.com/paper/2411.02704/) (2024)
- [Inference-Time Policy Steering through Human Interactions](https://mlsanity.com/paper/2411.16627/) (2024)
- [ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context](https://mlsanity.com/paper/2510.04246/) (2025)
- [Learning Long-Context Diffusion Policies via Past-Token Prediction](https://mlsanity.com/paper/2505.09561/) (2025)
- [TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning](https://mlsanity.com/paper/2412.10447/) (2024)
- [Robotic Visual Instruction](https://mlsanity.com/paper/2505.00693/) (2025)
- [ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting](https://mlsanity.com/paper/2410.17856/) (2024)
- [Generative Image as Action Models](https://mlsanity.com/paper/2407.07875/) (2024)
- [Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Models](https://mlsanity.com/paper/2502.10090/) (2025)
- [Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols](https://mlsanity.com/paper/2512.02787/) (2025)
- [Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches](https://mlsanity.com/paper/2503.11918/) (2025)
- [LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2608.03701/) (2026)
- [Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models](https://mlsanity.com/paper/2410.00231/) (2024)
- [ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment](https://mlsanity.com/paper/2503.02505/) (2025)
- [Chain-of-Modality: Learning Manipulation Programs from Multimodal Human Videos with Vision-Language-Models](https://mlsanity.com/paper/2504.13351/) (2025)
- [CrayonRobo: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2505.02166/) (2025)
- [BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation](https://mlsanity.com/paper/2406.10093/) (2024)
- [Behavioral Exploration: Learning to Explore via In-Context Adaptation](https://mlsanity.com/paper/2507.09041/) (2025)
- [Action-Sketcher: From Reasoning to Action via Visual Sketches for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2601.01618/) (2026)
- [Contact-Anchored Policies: Contact Conditioning Creates Strong Robot Utility Models](https://mlsanity.com/paper/2602.09017/) (2026)
- [Look Before Acting: Enhancing Vision Foundation Representations for Vision-Language-Action Models](https://mlsanity.com/paper/2603.15618/) (2026)
- [Object-centric proto-symbolic behavioural reasoning from pixels](https://mlsanity.com/paper/2411.17438/) (2024)
- [FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis](https://mlsanity.com/paper/2505.09109/) (2025)
- [Cross-Modal Instructions for Robot Motion Generation](https://mlsanity.com/paper/2509.21107/) (2025)
- [SketchPlan: Diffusion Based Drone Planning From Human Sketches](https://mlsanity.com/paper/2510.03545/) (2025)

## Abstract (arXiv)

Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality for goal specification in visual imitation learning. Sketches are easy for users to provide on the fly like language, but similar to images they can also help a downstream policy to be spatially-aware and even go beyond images to disambiguate task-relevant from task-irrelevant objects. We present RT-Sketch, a goal-conditioned policy for manipulation that takes a hand-drawn sketch of the desired scene as input, and outputs actions. We train RT-Sketch on a dataset of paired trajectories and corresponding synthetically generated goal sketches. We evaluate this approach on six manipulation skills involving tabletop object rearrangements on an articulated countertop. Experimentally we find that RT-Sketch is able to perform on a similar level to image or language-conditioned agents in straightforward settings, while achieving greater robustness when language goals are ambiguous or visual distractors are present. Additionally, we show that RT-Sketch has the capacity to interpret and act upon sketches with varied levels of specificity, ranging from minimal line drawings to detailed, colored drawings. For supplementary material and videos, please refer to our website: http://rt-sketch.github.io.