BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
- URL: http://arxiv.org/abs/2605.03452v1
- Date: Tue, 05 May 2026 07:35:09 GMT
- Title: BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
- Abstract summary: BifrostUMI is a portable, efficient, and robot-free data collection framework for humanoid robots.<n>We demonstrate the efficacy and versatility of the proposed framework across two distinct experimental scenarios.
- Score: 6.733928872257592
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: High-quality data collection is a fundamental cornerstone for training humanoid whole-body visuomotor policies. Current data acquisition paradigms predominantly rely on robot teleoperation, which is often hindered by limited hardware accessibility and low operational efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose BifrostUMI, a portable, efficient, and robot-free data collection framework tailored for humanoid robots. BifrostUMI leverages lightweight VR devices to capture human demonstrations as sparse keypoint trajectories while simultaneously recording wrist-mounted visual data. These multimodal data are subsequently utilized to train a high-level policy network that predicts future keypoint trajectories conditioned on the captured visual features. Through a robust keypoint retargeting pipeline, keypoint trajectories are precisely mapped onto the robot's morphology and executed via a whole-body controller. This approach enables the seamless transfer of diverse and agile behaviors from natural human demonstrations to humanoid embodiments. We demonstrate the efficacy and versatility of the proposed framework across two distinct experimental scenarios.
Related papers
- RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction [51.76737991200891]
Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets.<n>We introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions.<n>We propose PassGen, a generative pipeline that synthesizes realistic handover sequences while ensuring hand-object consistency.
arXiv Detail & Related papers (2026-08-13T09:55:59Z) - HumanoidUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation [6.733928872257592]
We propose HumanoidUMI, a portable and robot-free framework for humanoid whole-body data collection.<n>We use lightweight VR devices and UMI-inspired grippers to collect sparse human keypoint trajectories, wrist-view observations, and gripper actions.<n>Experiments in five real-world scenarios demonstrate the effectiveness of the proposed framework.
arXiv Detail & Related papers (2026-06-25T16:23:56Z) - OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
arXiv Detail & Related papers (2026-06-07T10:01:53Z) - WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations [10.024841990710177]
WARPED is a framework designed to synthesize realistic wrist-view observations from human demonstration videos.<n>A hand-object interaction pipeline is then employed to track the hand and manipulated object and retarget the trajectories to a robotic end-effector.<n>We demonstrate that WARPED achieves success rates comparable to policies trained on teleoperated demonstration data for five tabletop manipulation tasks.
arXiv Detail & Related papers (2026-04-12T20:40:59Z) - Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations [23.294838162593184]
We present GraspDreamer, a method to enable zero-shot functional grasping without labor-intensive data collection.<n>The key idea is that VGMs pre-trained on internet-scale human data implicitly encode generalized priors about how humans interact with the physical world.<n>Extensive experiments on the public benchmarks with different robot hands demonstrate the superior data efficiency and generalization performance of GraspDreamer.
arXiv Detail & Related papers (2026-04-08T18:52:16Z) - ZeroWBC: Learning Natural Visuomotor Humanoid Control Directly from Human Egocentric Video [52.78703020909145]
We introduce ZeroWBC, a novel framework that learns a natural humanoid visuomotor control policy directly from human egocentric videos.<n>Our approach first fine-tunes a Vision-Language Model (VLM) to predict future whole-body human motions based on text instructions and egocentric visual context.<n>Experiments on the Unitree G1 humanoid robot demonstrate that our method outperforms baseline approaches in motion naturalness and versatility.
arXiv Detail & Related papers (2026-03-10T04:19:43Z) - 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) - MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training [102.850162490626]
We propose MiVLA, a vision-language-action model empowered by human-robot mutual imitation pre-training.<n>We show that MiVLA achieves strong improved generalization capability, outperforming state-of-the-art VLAs.
arXiv Detail & Related papers (2025-12-17T12:59:41Z) - DemoHLM: From One Demonstration to Generalizable Humanoid Loco-Manipulation [29.519071338337685]
We present DemoHLM, a framework for humanoid loco-manipulation on a real humanoid robot from a single demonstration in simulation.<n>whole-body controller maps whole-body motion commands to joint torques and provides omnidirectional mobility for the humanoid robot.<n> Experiments show a positive correlation between the amount of synthetic data and policy performance.
arXiv Detail & Related papers (2025-10-13T10:49:40Z) - One-Shot Imitation under Mismatched Execution [7.060120660671016]
Human demonstrations are a powerful way to program robots to do long-horizon manipulation tasks.<n> translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movement styles and physical capabilities.<n>We propose RHyME, a novel framework that automatically pairs human and robot trajectories using sequence-level optimal transport cost functions.
arXiv Detail & Related papers (2024-09-10T16:11:57Z) - Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
arXiv Detail & Related papers (2023-06-09T07:22:12Z) - Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
Planning motions that take human comfort into account is not a part of the human-robot handover process.
We propose to generate smooth motions via an efficient model-predictive control framework.
We conduct human-to-robot handover experiments on a diverse set of objects with several users.
arXiv Detail & Related papers (2022-03-31T23:08:20Z) - Few-Shot Visual Grounding for Natural Human-Robot Interaction [0.0]
We propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user.
At the core of our system, we employ a multi-modal deep neural network for visual grounding.
We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets.
arXiv Detail & Related papers (2021-03-17T15:24:02Z)
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.