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PAPRAS: Plug-And-Play Robotic Arm System

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arxiv 2302.09655 v1 pith:HMEVYRFZ submitted 2023-02-19 cs.RO

Joohyung Kim , Dhruv C Mathur , Kazuki Shin , Sean Taylor This is my paper
classification cs.RO
keywords paprasroboticsystemsoftwarearchitecturecontrold-printeddemonstrations
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel robotic arm system, named PAPRAS (Plug-And-Play Robotic Arm System). PAPRAS consists of a portable robotic arm(s), docking mount(s), and software architecture including a control system. By analyzing the target task spaces at home, the dimensions and configuration of PAPRAS are determined. PAPRAS's arm is light (less than 6kg) with an optimized 3D-printed structure, and it has a high payload (3kg) as a human-arm-sized manipulator. A locking mechanism is embedded in the structure for better portability and the 3D-printed docking mount can be installed easily. PAPRAS's software architecture is developed on an open-source framework and optimized for low-latency multiagent-based distributed manipulator control. A process to create new demonstrations is presented to show PAPRAS's ease of use and efficiency. In the paper, simulations and hardware experiments are presented in various demonstrations, including sink-to-dishwasher manipulation, coffee making, mobile manipulation on a quadruped, and suit-up demo to validate the hardware and software design.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs

    cs.RO 2025-07 conditional novelty 6.0 of 10

    PAPRLE is a modular teleoperation ecosystem that pairs diverse input devices with arbitrary robot limb configurations and adds force feedback even when leader and follower morphologies differ.

  2. Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach

    cs.LG 2025-08 unverdicted novelty 5.0 of 10

    The claimed result is that source-component-shift adaptation splits cleanly into offline component learning via EM and online mixing-weight updates, cutting cumulative test loss by up to 67.4%.

  3. Leveraging OS-Level Primitives for Robotic Action Management

    cs.OS 2025-08 conditional novelty 4.0 of 10

    Applying OS-style exception handling, context caching, and replay to robotic action slices raises success rates 7x to 24x and cuts execution steps up to 74% for repetitive manipulation tasks, without retraining the VLA model.

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