SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation

• URL: http://arxiv.org/abs/2603.06280v1
• Date: Fri, 06 Mar 2026 13:40:30 GMT
• Title: SuperSuit: An Isomorphic Bimodal Interface for Scalable Mobile Manipulation
• Abstract summary: We present textbfSuperSuit, a bimodal data acquisition framework that supports both robot-in-the-loop teleoperation and active demonstration under a shared kinematic interface.<n>Real-world experiments on long-horizon mobile manipulation tasks show 2.6$times$ higher demonstration throughput in active mode compared to a teleoperation baseline, comparable policy performance when substituting teleoperation data with active demonstrations at fixed dataset size, and monotonic performance improvement as active data volume increases.
• Score: 8.367600706539774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality, long-horizon demonstrations are essential for embodied AI, yet acquiring such data for tightly coupled wheeled mobile manipulators remains a fundamental bottleneck. Unlike fixed-base systems, mobile manipulators require continuous coordination between $SE(2)$ locomotion and precise manipulation, exposing limitations in existing teleoperation and wearable interfaces. We present \textbf{SuperSuit}, a bimodal data acquisition framework that supports both robot-in-the-loop teleoperation and active demonstration under a shared kinematic interface. Both modalities produce structurally identical joint-space trajectories, enabling direct data mixing without modifying downstream policies. For locomotion, SuperSuit maps natural human stepping to continuous planar base velocities, eliminating discrete command switches. For manipulation, it employs a strictly isomorphic wearable arm in both modes, while policy training is formulated in a shift-invariant delta-joint representation to mitigate calibration offsets and structural compliance without inverse kinematics. Real-world experiments on long-horizon mobile manipulation tasks show 2.6$\times$ higher demonstration throughput in active mode compared to a teleoperation baseline, comparable policy performance when substituting teleoperation data with active demonstrations at fixed dataset size, and monotonic performance improvement as active data volume increases. These results indicate that consistent kinematic representations across collection modalities enable scalable data acquisition for long-horizon mobile manipulation.

Related papers

• CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements [63.40365657068892]
  Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning.<n>Motivated by this gap, we adopt an implicit, data-driven approach guided by fine-grained hand-pose motions.<n>In both simulation and real-robot experiments, our framework enables stable and precise hand-to-gripper transfer of complex spatial manipulations.
  arXiv  Detail & Related papers  (2026-09-07T13:47:22Z)
• ABot-M0.5: Unified Mobility-and-Manipulation World Action Model [27.262271208923995]
  ABot-M0.5 is built on the insight that mobile manipulation requires alignment at three levels: temporal granularity, action space, and train-test consistency.<n> Experiments on challenging mobile and fine-grained manipulation benchmarks demonstrate that ABot-M0.5 achieves state-of-the-art performance in both long-horizon task success and finegrained control accuracy.
  arXiv  Detail & Related papers  (2026-07-01T09:21:20Z)
• VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis [35.55805069125473]
  We propose a unified flow-matching-based dual-stream framework that jointly generates video and action under visual and language conditioning.<n>Across both simulated and real-world settings, VAG produces aligned video-action pairs with competitive prediction quality, supports executable trajectory replay, and provides useful synthetic pretraining data.
  arXiv  Detail & Related papers  (2026-04-10T13:59:54Z)
• OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation [60.609604885961716]
  We present textbfOmniViTac, a large-scale visuo-tactile-action dataset comprising $21,000+$ trajectories across $86$ tasks and $100+$ objects.<n>We propose textbf OmniVTA, a world-model-based visuo-tactile manipulation framework that integrates four tightly coupled modules.
  arXiv  Detail & Related papers  (2026-03-19T17:52:42Z)
• DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving [47.573692944838115]
  DriveMamba is a Task-Centric Scalable paradigm for efficient E2E-AD.<n>It integrates sequential task relation modeling, implicit correspondence learning and long-term temporal fusion into a single-stage Unified Mamba decoder.<n>Extensive experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superiority, generalizability and great efficiency of DriveMamba.
  arXiv  Detail & Related papers  (2026-02-09T11:48:29Z)
• FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos [109.99404241220039]
  We introduce FoundationMotion, a fully automated data curation pipeline that constructs large-scale motion datasets.<n>Our approach first detects and tracks objects in videos to extract their trajectories, then leverages these trajectories and video frames with Large Language Models.<n>We fine-tune open-source models including NVILA-Video-15B and Qwen2.5-7B, achieving substantial improvements in motion understanding without compromising performance.
  arXiv  Detail & Related papers  (2025-12-11T18:53:15Z)
• Precise Action-to-Video Generation Through Visual Action Prompts [62.951609704196485]
  Action-driven video generation faces a precision-generality trade-off.<n>Agent-centric action signals provide precision at the cost of cross-domain transferability.<n>We "render" actions into precise visual prompts as domain-agnostic representations.
  arXiv  Detail & Related papers  (2025-08-18T17:12:28Z)
• AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation [31.314066269767057]
  Mobile manipulation has attracted increasing attention for enabling language-conditioned robotic control in household tasks.<n>Existing methods fail to explicitly model the influence of the mobile base on manipulator control.<n>We propose the Adaptive Coordination Diffusion Transformer (AC-DiT) to enhance mobile base and manipulator coordination.
  arXiv  Detail & Related papers  (2025-07-02T17:59:54Z)
• A Unified Transformer-Based Framework with Pretraining For Whole Body Grasping Motion Generation [6.465569743109499]
  We present a novel transformer-based framework for whole-body grasping.<n>It addresses pose generation and motion infilling, enabling realistic and stable object interactions.<n>Our method outperforms state-of-the-art baselines in terms of coherence, stability, and visual realism.
  arXiv  Detail & Related papers  (2025-07-01T11:18:23Z)
• Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy [73.75271615101754]
  We present Dita, a scalable framework that leverages Transformer architectures to directly denoise continuous action sequences.<n>Dita employs in-context conditioning -- enabling fine-grained alignment between denoised actions and raw visual tokens from historical observations.<n>Dita effectively integrates cross-embodiment datasets across diverse camera perspectives, observation scenes, tasks, and action spaces.
  arXiv  Detail & Related papers  (2025-03-25T15:19:56Z)
• Diffusion Transformer Policy [48.50988753948537]
  We propose a large multi-modal diffusion transformer, dubbed as Diffusion Transformer Policy, to model continuous end-effector actions.<n>By leveraging the scaling capability of transformers, the proposed approach can effectively model continuous end-effector actions across large diverse robot datasets.
  arXiv  Detail & Related papers  (2024-10-21T12:43:54Z)
• Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations [96.03270112422514]
  We construct a Virtual Kinematic Chain (VKC) that consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. A mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem.
  arXiv  Detail & Related papers  (2021-08-03T02:59:41Z)

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.