# RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation

arXiv: https://arxiv.org/abs/2503.07771 · HTML: https://mlsanity.com/paper/2503.07771/

## Key facts

- Cited by 26 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 35 of its 66 references that are in the map.
- First posted on arXiv 10 Mar 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 35)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks](https://mlsanity.com/paper/2112.03227/) (2021)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [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)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023)
- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022)
- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://mlsanity.com/paper/2012.06733/) (2020)
- [Visual Imitation Made Easy](https://mlsanity.com/paper/2008.04899/) (2020)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [Feedback in Imitation Learning: The Three Regimes of Covariate Shift](https://mlsanity.com/paper/2102.02872/) (2021)
- [Interactive Imitation Learning in Robotics: A Survey](https://mlsanity.com/paper/2211.00600/) (2022)
- [Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization](https://mlsanity.com/paper/2202.10341/) (2022)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)

## Built on by (most cited first, 26 of 26)

- [Failure Prediction at Runtime for Generative Robot Policies](https://mlsanity.com/paper/2510.09459/) (2025)
- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://mlsanity.com/paper/2509.07953/) (2025)
- [RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks](https://mlsanity.com/paper/2603.11558/) (2026)
- [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025)
- [Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends](https://mlsanity.com/paper/2506.20966/) (2025)
- [InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning](https://mlsanity.com/paper/2601.14209/) (2026)
- [DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation](https://mlsanity.com/paper/2605.31286/) (2026)
- [HACTS: a Human-As-Copilot Teleoperation System for Robot Learning](https://mlsanity.com/paper/2503.24070/) (2025)
- [Real-world Reinforcement Learning from Suboptimal Interventions](https://mlsanity.com/paper/2512.24288/) (2025)
- [FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation](https://mlsanity.com/paper/2606.08555/) (2026)
- [Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidance](https://mlsanity.com/paper/2505.18793/) (2025)
- [ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training](https://mlsanity.com/paper/2602.12691/) (2026)
- [Human-in-the-loop Online Rejection Sampling for Robotic Manipulation](https://mlsanity.com/paper/2510.26406/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Society of Mind Meets Real-Time Strategy: A Hierarchical Multi-Agent Framework for Strategic Reasoning](https://mlsanity.com/paper/2508.06042/) (2025)
- [RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models](https://mlsanity.com/paper/2605.09410/) (2026)
- [Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention](https://mlsanity.com/paper/2605.15157/) (2026)
- [SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation](https://mlsanity.com/paper/2606.10305/) (2026)
- [Robot Self-Improvement via Human-Video Dynamics Models](https://mlsanity.com/paper/2606.21406/) (2026)
- [Human-Robot Copilot for Data-Efficient Imitation Learning](https://mlsanity.com/paper/2604.03613/) (2026)
- [One Demonstration Is Enough for Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2607.01651/) (2026)
- [DexPIE: Stable Dexterous Policy Improvement from Real-World Experience](https://mlsanity.com/paper/2606.09615/) (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)
- [Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning](https://mlsanity.com/paper/2610.05882/) (2026)
- [I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2610.17748/) (2026)

## Abstract (arXiv)

Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demonstration data for its simplicity in data collection. Interactive human teaching has appealing theoretical and practical properties, but they are not well supported by existing human-robot interfaces. This paper proposes a novel system that enables seamless control switching between human and an autonomous policy for bi-manual manipulation tasks, enabling more efficient learning of new tasks. This is achieved through a compliant, bilateral teleoperation system. Through simulation and hardware experiments, we demonstrate the value of our system in an interactive human teaching for learning complex bi-manual manipulation skills.