mlsanityData to 14 Oct 2026

A Comparison of Imitation Learning Algorithms for Bimanual Manipulation

arXiv 2408.06536 ↗First posted 13 Aug 2024cs.RO cs.LGcited by 8

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

Abstract (arXiv)

Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. Paper website: https://bimanual-imitation.github.io/

Key facts. Cited by 8 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 27 of its 61 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

27 of 61 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware23 Apr
  2. 2022Interactive Imitation Learning of Bimanual Movement Primitives28 Oct
  3. 2022A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies1 Aug
  4. 2020Deep Imitation Learning for Bimanual Robotic Manipulation11 Oct

Other references · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  6. 2015Trust Region Policy Optimizationcited by 2,580
  7. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  8. 2016Generative Adversarial Imitation Learningcited by 1,597
  9. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  10. 2023Open X-Embodiment: Robotic Learning Datasets and RT-X Modelscited by 862
  11. 2021Implicit Behavioral Cloningcited by 452
  12. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
11 more
  1. 2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 105
  2. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 84
  3. 2021What Matters for Adversarial Imitation Learning?cited by 70
  4. 2019Wasserstein Adversarial Imitation Learningcited by 60
  5. 2017A survey of robot learning from demonstrations for Human-Robot Collaborationcited by 38
  6. 2022Learning Bimanual Scooping Policies for Food Acquisitioncited by 20
  7. 2021Hyperparameter Selection for Imitation Learningcited by 17
  8. 2023Bi-Touch: Bimanual Tactile Manipulation with Sim-to-Real Deep Reinforcement Learningcited by 17
  9. 2022Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learningcited by 14
  10. 2023Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Modelscited by 10
  11. 2021A Pragmatic Look at Deep Imitation Learningcited by 9

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 6.

8 papers.

  1. 2024DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learningcited by 116
  2. 2025Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulationcited by 46
  3. 2025Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstrationcited by 34
  4. 2025Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketchescited by 9
  5. 2025LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Languagecited by 7
  6. 2024Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulationcited by 6
  7. 2025COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Graspingcited by 1
  8. 2025CroSTAta: Cross-State Transition Attention Transformer for Robotic Manipulationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
  2. 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
  3. 2024ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
  4. 2024BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation
  5. 2025Dexterous Manipulation through Imitation Learning: A Survey
  6. 2025Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models
  7. 2025RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments
  8. 2026RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation