mlsanityData to 7 Oct 2026

Imitation Learning with Additional Constraints on Motion Style using Parametric Bias

arXiv 2407.08057 ↗First posted 10 Jul 2024cs.ROcited by 1

On the map: Offline RL and imitation learning from data → Real-world robot offline RL

Abstract (arXiv)

Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 11 of its 27 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

11 of 27 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  5. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  6. 2017One-Shot Visual Imitation Learning via Meta-Learningcited by 225
  7. 2017OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learningcited by 40
  8. 2015Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Controlcited by 33
  9. 2020Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillationcited by 19
  10. 2024Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structurescited by 1
  11. 2024Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transitioncited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristicscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Constrained Style Learning from Imperfect Demonstrations under Task Optimality
  2. 2019A Linearly Constrained Nonparametric Framework for Imitation Learning
  3. 2023LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
  4. 2023Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning
  5. 2024Bi-Level Motion Imitation for Humanoid Robots
  6. 2023HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation
  7. 2024Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion
  8. 2019Towards Orientation Learning and Adaptation in Cartesian Space