# Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections

arXiv: https://arxiv.org/abs/2506.16685 · HTML: https://mlsanity.com/paper/2506.16685/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 66 references that are in the map.
- First posted on arXiv 20 Jun 2025.

## Builds on (most cited first, 30 of 48)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [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)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.16381/) (2023)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2410.21845/) (2024)
- [ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy](https://mlsanity.com/paper/2502.05450/) (2025)
- [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)
- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://mlsanity.com/paper/2012.06733/) (2020)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://mlsanity.com/paper/2211.08416/) (2022)
- [From Imitation to Refinement -- Residual RL for Precise Assembly](https://mlsanity.com/paper/2407.16677/) (2024)
- [Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone](https://mlsanity.com/paper/2412.06685/) (2024)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [XSkill: Cross Embodiment Skill Discovery](https://mlsanity.com/paper/2307.09955/) (2023)

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

- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://mlsanity.com/paper/2509.07953/) (2025)
- [Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://mlsanity.com/paper/2602.05073/) (2026)
- [BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models](https://mlsanity.com/paper/2512.12080/) (2025)
- [ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning](https://mlsanity.com/paper/2512.10946/) (2025)
- [FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation](https://mlsanity.com/paper/2602.22056/) (2026)
- [UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning](https://mlsanity.com/paper/2606.12372/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement](https://mlsanity.com/paper/2607.01111/) (2026)
- [What Makes Value Learning Efficient in Residual Reinforcement Learning?](https://mlsanity.com/paper/2602.10539/) (2026)
- [FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space](https://mlsanity.com/paper/2607.08877/) (2026)
- [Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement](https://mlsanity.com/paper/2606.18247/) (2026)
- [FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation](https://mlsanity.com/paper/2607.28596/) (2026)
- [Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention](https://mlsanity.com/paper/2605.15157/) (2026)
- [Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback](https://mlsanity.com/paper/2606.14218/) (2026)
- [Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections](https://mlsanity.com/paper/2606.01865/) (2026)
- [HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning](https://mlsanity.com/paper/2606.22860/) (2026)
- [One Demonstration Is Enough for Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2607.01651/) (2026)
- [OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies](https://mlsanity.com/paper/2607.03723/) (2026)
- [Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion](https://mlsanity.com/paper/2607.17257/) (2026)
- [VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations](https://mlsanity.com/paper/2608.06210/) (2026)
- [IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation](https://mlsanity.com/paper/2606.10818/) (2026)
- [ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies](https://mlsanity.com/paper/2606.28939/) (2026)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)
- [ForceDelta-VLA: Distilling Force-Conditioned ActionCorrections for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.18242/) (2026)
- [PAKT: Physically-Aligned Kinesthetic Teaching for Reinforcement Learning](https://mlsanity.com/paper/2609.25630/) (2026)
- [Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning](https://mlsanity.com/paper/2609.30462/) (2026)
- [BlenDAgger: Blended Shared Control for Interactive Imitation Learning](https://mlsanity.com/paper/2609.37599/) (2026)
- [Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.17161/) (2026)
- [I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2610.17748/) (2026)

## Abstract (arXiv)

We address key challenges in Dataset Aggregation (DAgger) for real-world contact-rich manipulation: how to collect informative human correction data and how to effectively update policies with this new data. We introduce Compliant Residual DAgger (CR-DAgger), which contains two novel components: 1) a Compliant Intervention Interface that leverages compliance control, allowing humans to provide gentle, accurate delta action corrections without interrupting the ongoing robot policy execution; and 2) a Compliant Residual Policy formulation that learns from human corrections while incorporating force feedback and force control. Our system significantly enhances performance on precise contact-rich manipulation tasks using minimal correction data, improving base policy success rates by 64% on four challenging tasks (book flipping, belt assembly, cable routing, and gear insertion) while outperforming both retraining-from-scratch and finetuning approaches. Through extensive real-world experiments, we provide practical guidance for implementing effective DAgger in real-world robot learning tasks. Result videos are available at: https://compliant-residual-dagger.github.io