mlsanityData to 7 Oct 2026

RAPID: Reconfigurable, Adaptive Platform for Iterative Design

arXiv 2602.06653 ↗First posted 6 Feb 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

Developing robotic manipulation policies is iterative and hypothesis-driven: researchers test tactile sensing, gripper geometries, and sensor placements through real-world data collection and training. Yet even minor end-effector changes often require mechanical refitting and system re-integration, slowing iteration. We present RAPID, a full-stack reconfigurable platform designed to reduce this friction. RAPID is built around a tool-free, modular hardware architecture that unifies handheld data collection and robot deployment, and a matching software stack that maintains real-time awareness of the underlying hardware configuration through a driver-level Physical Mask derived from USB events. This modular hardware architecture reduces reconfiguration to seconds and makes systematic multi-modal ablation studies practical, allowing researchers to sweep diverse gripper and sensing configurations without repeated system bring-up. The Physical Mask exposes modality presence as an explicit runtime signal, enabling auto-configuration and graceful degradation under sensor hot-plug events, so policies can continue executing when sensors are physically added or removed. System-centric experiments show that RAPID reduces the setup time for multi-modal configurations by two orders of magnitude compared to traditional workflows and preserves policy execution under runtime sensor hot-unplug events. The hardware designs, drivers, and software stack are open-sourced at https://rapid-kit.github.io/ .

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 53 references that are in the map. First posted on arXiv 6 Feb 2026.

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34 of 53 references.

References · 34

  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,730
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  4. 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 377
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  7. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 190
  8. 2023GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulatorscited by 180
  9. 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 164
  10. 2024UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllerscited by 67
  11. 2024Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progresscited by 63
  12. 2025DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulationcited by 58
22 more
  1. 2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policiescited by 49
  2. 2022Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robotscited by 44
  3. 2022Visuo-Tactile Transformers for Manipulationcited by 43
  4. 2024FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Datasetcited by 36
  5. 2025Failure Prediction at Runtime for Generative Robot Policiescited by 36
  6. 2019Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learningcited by 34
  7. 2024FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulationcited by 32
  8. 2020Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learningcited by 29
  9. 2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 22
  10. 2017Learning End-to-end Multimodal Sensor Policies for Autonomous Navigationcited by 16
  11. 2025exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representationcited by 14
  12. 2022Bayesian Imitation Learning for End-to-End Mobile Manipulationcited by 13
  13. 2019Evolving embodied intelligence from materials to machinescited by 11
  14. 2019Scalable sim-to-real transfer of soft robot designscited by 11
  15. 2025MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learningcited by 8
  16. 2026TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Taskscited by 8
  17. 2025Modality-Composable Diffusion Policy via Inference-Time Distribution-level Compositioncited by 5
  18. 2018Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitationscited by 4
  19. 2025Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Graspingcited by 4
  20. 2019Sim2real gap is non-monotonic with robot complexity for morphology-in-the-loop flapping wing designcited by 3
  21. 2025DIPOLE: Fusing Vision and Geometry for Robust Visuomotor Generalizationcited by 2
  22. 2025FORTE: Tactile Force and Slip Sensing on Compliant Fingers for Delicate Manipulationcited by 1

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