mlsanityData to 7 Oct 2026

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

arXiv 2503.09018 ↗First posted 12 Mar 2025cs.RO cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

23 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models6 Nov
  2. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation26 Apr
  3. 2021Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation9 Dec
  4. 2020Human-in-the-Loop Imitation Learning using Remote Teleoperation12 Dec
  5. 2020Visual Imitation Made Easy11 Aug

Other references · 18

  1. 2018Behavioral Cloning from Observationcited by 482
  2. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  3. 2024Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robotscited by 373
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 245
  5. 2017DART: Noise Injection for Robust Imitation Learningcited by 187
  6. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  7. 2019AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videoscited by 105
  8. 2023On Bringing Robots Homecited by 101
  9. 2018EnsembleDAgger: A Bayesian Approach to Safe Imitation Learningcited by 96
  10. 2023Video Prediction Models as Rewards for Reinforcement Learningcited by 76
  11. 2021Visual Adversarial Imitation Learning using Variational Modelscited by 42
  12. 2019Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systemscited by 32
6 more
  1. 2021Learning from Imperfect Demonstrations from Agents with Varying Dynamicscited by 26
  2. 2021Learning Feasibility to Imitate Demonstrators with Different Dynamicscited by 10
  3. 2022Disturbance-Injected Robust Imitation Learning with Task Achievementcited by 8
  4. 2022Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulationcited by 8
  5. 2024Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Taskscited by 5
  6. 2023Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Taskscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Feasibility-aware Imitation Learning from Observation with Multimodal Feedbackcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2020Concurrent Training Improves the Performance of Behavioral Cloning from Observation
  2. 2025When a Robot is More Capable than a Human: Learning from Constrained Demonstrators
  3. 2022Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
  4. 2024VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
  5. 2026It's Not Just More Demos: Counterfactual Action Sensitivity Coverage for Data-Efficient Robust Robot Imitation
  6. 2024ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
  7. 2022Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
  8. 2025Action Chunking and Exploratory Data Collection Yield Exponential Improvements in Behavior Cloning for Continuous Control