# Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface

arXiv: https://arxiv.org/abs/2503.09018 · HTML: https://mlsanity.com/paper/2503.09018/

## Key facts

- Cited by 1 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 23 of its 30 references that are in the map.
- First posted on arXiv 12 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, 23 of 23)

- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [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)
- [AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos](https://mlsanity.com/paper/1912.04443/) (2019)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning](https://mlsanity.com/paper/1807.08364/) (2018)
- [From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation](https://mlsanity.com/paper/2204.12490/) (2022)
- [Video Prediction Models as Rewards for Reinforcement Learning](https://mlsanity.com/paper/2305.14343/) (2023)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [Visual Adversarial Imitation Learning using Variational Models](https://mlsanity.com/paper/2107.08829/) (2021)
- [Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems](https://mlsanity.com/paper/1907.04489/) (2019)
- [Learning from Imperfect Demonstrations from Agents with Varying Dynamics](https://mlsanity.com/paper/2103.05910/) (2021)
- [Learning Feasibility to Imitate Demonstrators with Different Dynamics](https://mlsanity.com/paper/2110.15142/) (2021)
- [Disturbance-Injected Robust Imitation Learning with Task Achievement](https://mlsanity.com/paper/2205.04195/) (2022)
- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation](https://mlsanity.com/paper/2211.03393/) (2022)
- [Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks](https://mlsanity.com/paper/2402.13466/) (2024)
- [Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks](https://mlsanity.com/paper/2303.12375/) (2023)
- [Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models](https://mlsanity.com/paper/2211.03020/) (2022)

## Built on by (most cited first, 1 of 1)

- [Feasibility-aware Imitation Learning from Observation with Multimodal Feedback](https://mlsanity.com/paper/2602.15351/) (2026)

## Abstract (arXiv)

Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in human and robot movement characteristics, a human expert might unintentionally demonstrate an action that the robot cannot execute. We propose feasibility-aware behavior cloning from observation (FABCO). In the FABCO framework, the feasibility of each demonstration is assessed using the robot's pre-trained forward and inverse dynamics models. This feasibility information is provided as visual feedback to the demonstrators, encouraging them to refine their demonstrations. During policy learning, estimated feasibility serves as a weight for the demonstration data, improving both the data efficiency and the robustness of the learned policy. We experimentally validated FABCO's effectiveness by applying it to a pipette insertion task involving a pipette and a vial. Four participants assessed the impact of the feasibility feedback and the weighted policy learning in FABCO. Additionally, we used the NASA Task Load Index (NASA-TLX) to evaluate the workload induced by demonstrations with visual feedback.