HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning

• URL: http://arxiv.org/abs/2605.27724v1
• Date: Tue, 26 May 2026 21:57:11 GMT
• Title: HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
• Abstract summary: We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data.<n>Our method adapts contact-rich whole-body skills from a handful of source demonstrations to new states.<n>We show that whole-body visuomotor policies co-trained with data generated by HumanoidMimicGen outperform those trained only on real-world data by 20%.
• Score: 52.022681285103126
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations, which are time-intensive and difficult to collect via teleoperation. Existing data-generation algorithms can automatically synthesize demonstrations for manipulators, but they are ineffective on humanoids because their high-dimensional composite action spaces involve arms, legs, and torsos. We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data. Our method adapts contact-rich whole-body skills from a handful of source demonstrations to new states, generalizing across changes in object pose. By interleaving these single- and dual-arm skills with whole-body locomotion and manipulation planning, the method generates stable, collision-free data across diverse scenes and layouts. To evaluate our approach, we introduce a new simulated loco-manipulation benchmark containing nine diverse tasks that test humanoid loco-manipulation capabilities. There, we demonstrate that HumanoidMimicGen automatically generates large datasets for imitation learning and enables a systematic study of how data generation and policy learning decisions impact model performance. We show that whole-body visuomotor policies co-trained with data generated by HumanoidMimicGen outperform those trained only on real-world data by 20%.

Related papers

• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World [63.2070298441355]
  EgoVerse is a collaborative platform for human data-driven robot learning.<n>We release 1,362 hours (80k episodes) of human demonstrations spanning 1,965 tasks, 240 scenes, and 2,087 unique demonstrators.<n>We conduct a large-scale study of human-to-robot transfer with experiments replicated across multiple labs, tasks, and robot embodiments.
  arXiv  Detail & Related papers  (2026-04-08T21:27:06Z)
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration [67.13034606664333]
  EgoHumanoid is the first framework to co-train a vision-language-action policy using egocentric human demonstrations.<n>A portable system for scalable human data collection is developed.
  arXiv  Detail & Related papers  (2026-02-10T18:59:03Z)
• Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation [16.701354625940308]
  Humanoid Everyday is a large-scale and diverse humanoid manipulation dataset.<n>It aggregates high-quality multimodal sensory data, including RGB, depth, LiDAR, and tactile inputs, together with natural language annotations.<n>We conduct an analysis of representative policy learning methods on our dataset, providing insights into their strengths and limitations.
  arXiv  Detail & Related papers  (2025-10-09T20:43:27Z)
• Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration [9.42179962375058]
  We propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype.<n>The model learns behavior primitives from human demonstrations through adversarial imitation, and complex robot structures are decomposed into functional components.<n>Our framework is validated on five humanoid robots with diverse configurations.
  arXiv  Detail & Related papers  (2024-12-19T18:41:45Z)
• DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning [42.88605563822155]
  We present a large-scale automated data generation system that synthesizes trajectories from human demonstrations for humanoid robots with dexterous hands.<n>We generate 21K demos across these tasks from just 60 source human demos.<n>We also present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task.
  arXiv  Detail & Related papers  (2024-10-31T17:48:45Z)
• VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections [10.49712834719005]
  Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions.<n>Despite its potential, the data collection process for IL remains a significant challenge due to logistical difficulties and high costs associated with obtaining high-quality demonstrations.<n>We propose a large-scale data generation from a handful of demonstrations through data augmentation in simulation.
  arXiv  Detail & Related papers  (2024-07-30T23:29:47Z)
• MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations [55.549956643032836]
  MimicGen is a system for automatically synthesizing large-scale, rich datasets from only a small number of human demonstrations. We show that robot agents can be effectively trained on this generated dataset by imitation learning to achieve strong performance in long-horizon and high-precision tasks.
  arXiv  Detail & Related papers  (2023-10-26T17:17:31Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.