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AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild

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arxiv 2309.14975 v2 pith:HGE5KXL5 submitted 2023-09-26 cs.RO

AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild

Hongjie Fang , Hao-Shu Fang , Yiming Wang , Jieji Ren , Jingjing Chen , Ruo Zhang , Weiming Wang , Cewu Lu This is my paper
classification cs.RO
keywords airexodemonstrationsin-the-wildlearnrobotsteleoperateddataeven
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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While humans can use parts of their arms other than the hands for manipulations like gathering and supporting, whether robots can effectively learn and perform the same type of operations remains relatively unexplored. As these manipulations require joint-level control to regulate the complete poses of the robots, we develop AirExo, a low-cost, adaptable, and portable dual-arm exoskeleton, for teleoperation and demonstration collection. As collecting teleoperated data is expensive and time-consuming, we further leverage AirExo to collect cheap in-the-wild demonstrations at scale. Under our in-the-wild learning framework, we show that with only 3 minutes of the teleoperated demonstrations, augmented by diverse and extensive in-the-wild data collected by AirExo, robots can learn a policy that is comparable to or even better than one learned from teleoperated demonstrations lasting over 20 minutes. Experiments demonstrate that our approach enables the model to learn a more general and robust policy across the various stages of the task, enhancing the success rates in task completion even with the presence of disturbances. Project website: https://airexo.github.io/

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Cited by 4 Pith papers

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

  1. Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

    cs.RO 2024-02 conditional novelty 7.0

    UMI enables zero-shot deployment of robot manipulation policies trained solely on portable human demonstrations captured with custom handheld grippers, supporting dynamic bimanual tasks across novel environments and objects.

  2. HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

    cs.RO 2026-07 conditional novelty 6.0

    Robot-free HiFi-UMI demonstrations can replace teleoperated real-robot data in post-training: three policy backbones matched in-domain teleoperation within 3.1 percentage points, including 85% success on a precision i...

  3. Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

    cs.RO 2024-01 conditional novelty 6.0

    A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.

  4. A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation

    cs.RO 2025-07 accept novelty 5.0

    Multi-task pretraining of diffusion policies on diverse robot data produces more successful, robust, and data-efficient policies for dexterous manipulation than single-task baselines, with performance scaling with pre...