MobileManiBench: Simplifying Model Verification for Mobile Manipulation

• URL: http://arxiv.org/abs/2602.05233v1
• Date: Thu, 05 Feb 2026 02:49:52 GMT
• Title: MobileManiBench: Simplifying Model Verification for Mobile Manipulation
• Abstract summary: MobileManiBench is a large-scale benchmark for mobile-based robotic manipulation.<n>MobileManiBench features 2 mobile platforms (parallel-gripper and dexterous-hand robots), 2 synchronized cameras (head and right wrist), 630 objects in 20 categories, 5 skills (open, close, pull, push, pick) with over 100 tasks performed in 100 realistic scenes, yielding 300K trajectories.
• Score: 70.30578259859512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action models have advanced robotic manipulation but remain constrained by reliance on the large, teleoperation-collected datasets dominated by the static, tabletop scenes. We propose a simulation-first framework to verify VLA architectures before real-world deployment and introduce MobileManiBench, a large-scale benchmark for mobile-based robotic manipulation. Built on NVIDIA Isaac Sim and powered by reinforcement learning, our pipeline autonomously generates diverse manipulation trajectories with rich annotations (language instructions, multi-view RGB-depth-segmentation images, synchronized object/robot states and actions). MobileManiBench features 2 mobile platforms (parallel-gripper and dexterous-hand robots), 2 synchronized cameras (head and right wrist), 630 objects in 20 categories, 5 skills (open, close, pull, push, pick) with over 100 tasks performed in 100 realistic scenes, yielding 300K trajectories. This design enables controlled, scalable studies of robot embodiments, sensing modalities, and policy architectures, accelerating research on data efficiency and generalization. We benchmark representative VLA models and report insights into perception, reasoning, and control in complex simulated environments.

Related papers

• Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation [55.724481205135675]
  Mobile manipulation poses two key challenges for vision-language-action policies.<n>We develop a whole-body teleoperation system and a panoramic-aware VLA policy.<n>We propose PanoVLA, a panorama-aware vision-language-action policy for mobile bimanual manipulation.
  arXiv  Detail & Related papers  (2026-08-03T14:03:26Z)
• Worlds in One Demo: A Synthetic Data Engine for Learning Open-World Mobile Manipulation [7.87892459538874]
  We introduce WANDA: learning open-World mobile mANipulation from one demonstration via a synthetic DAta engine.<n>We apply Corrective State Expansion to increase the robot and object state diversity at different stages of mobile manipulation.<n>Experiments show that policies trained with WANDA achieve long-horizon robustness, broad spatial generalization and cross-environment generalization.
  arXiv  Detail & Related papers  (2026-07-14T18:04:58Z)
• Scalable Trajectory Generation for Whole-Body Mobile Manipulation [10.909540204939598]
  We introduce AutoMoMa, a GPU-accelerated framework that unifies AKR modeling, base, arm, and object kinematics into a single chain.<n>AutoMoMa produces a dataset of over 500k physically valid trajectories spanning 330 scenes, diverse articulated objects, and multiple robot embodiments.
  arXiv  Detail & Related papers  (2026-04-14T10:47:06Z)
• RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence [59.39508501733987]
  We present RoboMIND 2.0, a comprehensive real-world dataset comprising over 310K dual-arm manipulation trajectories.<n>To support research in contact-rich and spatially extended tasks, the dataset incorporates 12K tactile-enhanced episodes and 20K mobile manipulation trajectories.<n>To fully exploit the potential of RoboMIND 2.0, we propose MIND-2 system, a hierarchical dual-system frame-work optimized via offline reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-31T05:59:40Z)
• FastUMI-100K: Advancing Data-driven Robotic Manipulation with a Large-scale UMI-style Dataset [55.66606167502093]
  We present FastUMI-100K, a large-scale UMI-style multimodal demonstration dataset.<n>FastUMI-100K offers a more scalable, flexible, and adaptable solution to fulfill the diverse requirements of real-world robot demonstration data.<n>Our dataset integrates multimodal streams, including end-effector states, multi-view wrist-mounted fisheye images and textual annotations.
  arXiv  Detail & Related papers  (2025-10-09T09:57:25Z)
• RoboPearls: Editable Video Simulation for Robot Manipulation [81.18434338506621]
  RoboPearls is an editable video simulation framework for robotic manipulation.<n>Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations.<n>We conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot.
  arXiv  Detail & Related papers  (2025-06-28T05:03:31Z)
• λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics [11.901933884058021]
  We introduce the LAMBDA benchmark-Long-horizon Actions for Mobile-manipulation Benchmarking of Directed Activities.<n>Our benchmark includes 571 human-collected demonstrations that provide realism and diversity in simulated and real-world settings.<n>We find that learning methods, even when pretrained, yield lower success rates, while a neuro-symbolic method performs significantly better and requires less data.
  arXiv  Detail & Related papers  (2024-11-28T19:31:50Z)
• M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes [66.44171200767839]
  M3Bench is a new benchmark for whole-body motion generation in mobile manipulation tasks.<n>M3Bench features 30,000 object rearrangement tasks across 119 diverse scenes.<n>M3Bench and M3BenchMaker aim to advance robotics research toward more adaptive and capable mobile manipulation.
  arXiv  Detail & Related papers  (2024-10-09T08:38:21Z)
• IRASim: A Fine-Grained World Model for Robot Manipulation [24.591694756757278]
  We present IRASim, a novel world model capable of generating videos with fine-grained robot-object interaction details.<n>We train a diffusion transformer and introduce a novel frame-level action-conditioning module within each transformer block to explicitly model and strengthen the action-frame alignment.
  arXiv  Detail & Related papers  (2024-06-20T17:50:16Z)

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.