ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations

• URL: http://arxiv.org/abs/2510.01607v1
• Date: Thu, 02 Oct 2025 02:44:21 GMT
• Title: ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
• Authors: Qiyuan Zeng, Chengmeng Li, Jude St. John, Zhongyi Zhou, Junjie Wen, Guorui Feng, Yichen Zhu, Yi Xu,
• Abstract summary: We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation.<n>ActiveUMI couples a portable VR teleoperation kit with sensorized controllers that mirror the robot's end-effectors.<n>By recording an operator's deliberate head movements via a head-mounted display, our system learns the crucial link between visual attention and manipulation.
• Score: 32.570602111692914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation. ActiveUMI couples a portable VR teleoperation kit with sensorized controllers that mirror the robot's end-effectors, bridging human-robot kinematics via precise pose alignment. To ensure mobility and data quality, we introduce several key techniques, including immersive 3D model rendering, a self-contained wearable computer, and efficient calibration methods. ActiveUMI's defining feature is its capture of active, egocentric perception. By recording an operator's deliberate head movements via a head-mounted display, our system learns the crucial link between visual attention and manipulation. We evaluate ActiveUMI on six challenging bimanual tasks. Policies trained exclusively on ActiveUMI data achieve an average success rate of 70\% on in-distribution tasks and demonstrate strong generalization, retaining a 56\% success rate when tested on novel objects and in new environments. Our results demonstrate that portable data collection systems, when coupled with learned active perception, provide an effective and scalable pathway toward creating generalizable and highly capable real-world robot policies.

Related papers

• End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection [10.217810309422232]
  We propose a framework that divides control between macro and micro motions.<n>A human operator guides the robot's arm pose through intuitive VR teleoperation.<n>An autonomous DexGrasp-VLA policy handles fine-grained hand control using real-time tactile and visual feedback.
  arXiv  Detail & Related papers  (2025-10-31T16:12:02Z)
• Recognizing Actions from Robotic View for Natural Human-Robot Interaction [52.00935005918032]
  Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationary.<n>Existing benchmarks for N-HRI fail to address the unique complexities in N-HRI due to limited data, modalities, task categories, and diversity of subjects and environments.<n>We introduce (Action from Robotic View) a large-scale dataset for perception-centric robotic views prevalent in mobile service robots.
  arXiv  Detail & Related papers  (2025-07-30T09:48:34Z)
• TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies [95.30717188630432]
  We introduce visual trace prompting to facilitate VLA models' spatial-temporal awareness for action prediction.<n>We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories.<n>We present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset.
  arXiv  Detail & Related papers  (2024-12-13T18:40:51Z)
• Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning [24.079032278280447]
  We propose an approach that combines batch reinforcement learning (RL) with model-predictive control (MPC)<n>We validate the proposed approach through extensive simulated and real-world experiments on a Franka Panda robot performing the robot waiter task.
  arXiv  Detail & Related papers  (2024-11-27T03:33:42Z)
• Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets [24.77850617214567]
  We propose a foundation representation learning framework capturing both visual features and the dynamics information such as actions and proprioceptions of manipulation tasks. Specifically, we pre-train a visual encoder on the DROID robotic dataset and leverage motion-relevant data such as robot proprioceptive states and actions. We introduce a novel contrastive loss that aligns visual observations with the robot's proprioceptive state-action dynamics, combined with a behavior cloning (BC)-like actor loss to predict actions during pre-training, along with a time contrastive loss.
  arXiv  Detail & Related papers  (2024-10-29T17:58:13Z)
• Latent Action Pretraining from Videos [156.88613023078778]
  We introduce Latent Action Pretraining for general Action models (LAPA)<n>LAPA is an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot action labels.<n>We propose a method to learn from internet-scale videos that do not have robot action labels.
  arXiv  Detail & Related papers  (2024-10-15T16:28:09Z)
• Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost [8.71539730969424]
  MoMa-Teleop is a novel teleoperation method that infers end-effector motions from existing interfaces.<n>We demonstrate that our approach results in a significant reduction in task completion time across a variety of robots and tasks.
  arXiv  Detail & Related papers  (2024-09-23T15:09:45Z)
• 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)
• Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
  We introduce a novel system for joint learning between human operators and robots. It enables human operators to share control of a robot end-effector with a learned assistive agent. It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
  arXiv  Detail & Related papers  (2024-06-29T03:37:29Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)

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.