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AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons
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Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on costly and inflexible robot platforms. In-the-wild demonstrations offer a promising alternative, but existing collection devices have key limitations: handheld setups offer limited observational coverage, and whole-body systems often require fine-tuning with robot data due to domain gaps. To address these challenges, we present AirExo-2, a low-cost exoskeleton system for large-scale in-the-wild data collection, along with several adaptors that transform collected data into pseudo-robot demonstrations suitable for policy learning. We further introduce RISE-2, a generalizable imitation learning policy that fuses 3D spatial and 2D semantic perception for robust manipulations. Experiments show that RISE-2 outperforms prior state-of-the-art methods on both in-domain and generalization evaluations. Trained solely on adapted in-the-wild data produced by AirExo-2, the RISE-2 policy achieves comparable performance to the policy trained with teleoperated data, highlighting the effectiveness and potential of AirExo-2 for scalable and generalizable imitation learning.
Forward citations
Cited by 6 Pith papers
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DexDirect: Direct Kinesthetic Arm Guidance for Efficient Dexterous Demonstration Collection
A hybrid kinesthetic-arm-plus-webcam-hand teleoperation interface achieved 17x/3x higher demonstration throughput than vision baselines and trained a 90%-success pick-and-place policy in a ten-person study.
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HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone
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...
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ModPack: An Extensible Teleoperation Interface for Bimanual Mobile Manipulation
A modular backpack-based teleoperation interface enables bimanual mobile manipulation with haptic feedback and active perception across multiple robot platforms.
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Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions
A semantic keypoint graph matched to novel objects lets imitation-learned manipulation policies generalize with a quarter of the demonstrations.
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KAI: A Kinematic-Aware Interface for Data-Efficient Articulated Object Manipulation
KAI, a keypoint-and-displacement intermediate with geometric joint priors, matches or beats articulated-manipulation baselines at half the demo data and supports human-video co-training.
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Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics
Motion-only imitation learning reproduces walking kinematics but produces inaccurate ground reaction forces and joint moments; adding GRF and center-of-pressure rewards brings simulated kinetics closer to inverse dynamics.
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