Online Imitation Learning for Manipulation via Decaying Relative Correction through Teleoperation
On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning
Abstract (arXiv)
Teleoperated robotic manipulators enable the collection of demonstration data, which can be used to train control policies through imitation learning. However, such methods can require significant amounts of training data to develop robust policies or adapt them to new and unseen tasks. While expert feedback can significantly enhance policy performance, providing continuous feedback can be cognitively demanding and time-consuming for experts. To address this challenge, we propose to use a cable-driven teleoperation system which can provide spatial corrections with 6 degree of freedom to the trajectories generated by a policy model. Specifically, we propose a correction method termed Decaying Relative Correction (DRC) which is based upon the spatial offset vector provided by the expert and exists temporarily, and which reduces the intervention steps required by an expert. Our results demonstrate that DRC reduces the required expert intervention rate by 30% compared to a standard absolute corrective method. Furthermore, we show that integrating DRC within an online imitation learning framework rapidly increases the success rate of manipulation tasks such as raspberry harvesting and cloth wiping.
Key facts. Cited by 2 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 14 of its 19 references that are in the map. First posted on arXiv 19 Mar 2025.
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14 of 19 references.
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Other references · 13
- 2017Deep reinforcement learning from human preferencescited by 3,912
- 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
- 2018Learning to Reweight Examples for Robust Deep Learningcited by 675
- 2018An Algorithmic Perspective on Imitation Learningcited by 459
- 2024Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robotscited by 394
- 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 328
- 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
- 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 260
- 2019A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithmscited by 142
- 2022Interactive Imitation Learning in Robotics: A Surveycited by 55
- 2021Correct Me if I am Wrong: Interactive Learning for Robotic Manipulationcited by 24
- 2020Interactive Imitation Learning in State-Spacecited by 10
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
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- 2026ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
- 2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation
- 2020Human-in-the-Loop Imitation Learning using Remote Teleoperation
- 2021Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics
- 2026Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion
- 2026Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
- 2025Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger
- 2026Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations ↗