Scalable Trajectory Generation for Whole-Body Mobile Manipulation

• URL: http://arxiv.org/abs/2604.12565v1
• Date: Tue, 14 Apr 2026 10:47:06 GMT
• Title: Scalable Trajectory Generation for Whole-Body Mobile Manipulation
• Abstract summary: 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.
• Score: 10.909540204939598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed-base manipulation. Yet existing acquisition methods, including teleoperation and planning, are either labor-intensive or computationally prohibitive at scale. The core bottleneck is the lack of a scalable pipeline for generating large-scale, physically valid, coordinated trajectory data across diverse embodiments and environments. Here we introduce AutoMoMa, a GPU-accelerated framework that unifies AKR modeling, which consolidates base, arm, and object kinematics into a single chain, with parallelized trajectory optimization. AutoMoMa achieves 5,000 episodes per GPU-hour (over $80\times$ faster than CPU-based baselines), producing a dataset of over 500k physically valid trajectories spanning 330 scenes, diverse articulated objects, and multiple robot embodiments. Prior datasets were forced to compromise on scale, diversity, or kinematic fidelity; AutoMoMa addresses all three simultaneously. Training downstream IL policies further reveals that even a single articulated-object task requires tens of thousands of demonstrations for SOTA methods to reach $\approx 80\%$ success, confirming that data scarcity -- not algorithmic limitations -- has been the binding constraint. AutoMoMa thus bridges high-performance planning and reliable IL-based control, providing the infrastructure previously missing for coordinated mobile manipulation research. By making large-scale, kinematically valid training data practical, AutoMoMa showcases generalizable whole-body robot policies capable of operating in the diverse, unstructured settings of the real world.

Related papers

• Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report [25.18747802029395]
  We present ABot-C0, a generalist motion-control system for quadruped robots.<n>We construct a data pyramid through conditional video-generation, annotated motion capture, and human design, producing 16,074 physically feasible motion clips.<n>We then go a step further toward robust all-terrain locomotion by adopting a three-stage privileged-to-perceptive framework.
  arXiv  Detail & Related papers  (2026-07-08T13:04:07Z)
• X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting [3.134050607080983]
  Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks.<n>We propose a hierarchical whole-body teleoperation framework driven by a single XR device.<n>We show higher success rates on whole-body control tasks for both a humanoid and a mobile manipulator.
  arXiv  Detail & Related papers  (2026-06-06T01:50:59Z)
• M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking [45.636871702422134]
  Multi-Modal Mimic is a versatile whole-body control framework for humanoid robots.<n>It unifies heterogeneous motion reference modalities, including robot joint angles, human pose trajectories, and end-effector poses.<n>It achieves sim-to-real transfer across multiple motion reference modalities without modality-specific retraining.
  arXiv  Detail & Related papers  (2026-06-03T12:52:37Z)
• AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling [60.13338774249153]
  We introduce OmniHuMo, a dataset of over 5,000 hours of motion and 3.2 million sequences with precisely aligned multimodal annotations.<n>We propose AnyMo, a unified multimodal framework combining a Residual FSQ-based motion tokenizer with a scalable masked modeling transformer.<n>Experiments show that AnyMo achieves high-fidelity synthesis while offering flexible control over both spatial and stylistic attributes.
  arXiv  Detail & Related papers  (2026-05-28T07:15:19Z)
• ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning [31.000965640377128]
  ABot-M0 is a framework that builds a systematic data curation pipeline.<n>It enables end-to-end transformation of heterogeneous raw data into unified, efficient representations.<n>ABot-M0 supports modular perception via a dual-stream mechanism.
  arXiv  Detail & Related papers  (2026-02-11T16:47:01Z)
• MobileManiBench: Simplifying Model Verification for Mobile Manipulation [70.30578259859512]
  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.
  arXiv  Detail & Related papers  (2026-02-05T02:49:52Z)
• 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)
• Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation [0.6533458718563319]
  This paper presents a framework designed to enhance the grasping capabilities of quadrupeds equipped with arms.<n>We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects.<n>This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras.<n>We validated the complete framework on a four-legged robot.
  arXiv  Detail & Related papers  (2025-08-24T17:47:56Z)
• AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs [68.99086112477565]
  Transformer-based large language models (LLMs) have demonstrated exceptional capabilities in sequence modeling and text generation.<n>Existing heterogeneous training methods significantly expand the scale of trainable models but introduce substantial communication overheads and CPU workloads.<n>We propose AutoHete, an automatic and efficient heterogeneous training system compatible with both single- GPU and multi- GPU environments.
  arXiv  Detail & Related papers  (2025-02-27T14:46:22Z)
• 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)

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.