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Experiment-free exoskeleton assistance via learning in simulation


Exoskeletons have enormous potential to improve human locomotive performance1,2,3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.

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Fig. 1: Experiment-free optimization of exoskeleton assistance through learning in simulation.
Fig. 2: Learning-in-simulation framework.
Fig. 3: Generalizable and adaptive assistive torque by the learned controller.
Fig. 4: Representative assistive torque during various activities and locomotion transitions.
Fig. 5: Reduction in metabolic rate during walking, running and stair climbing.

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Data availability

All data supporting the findings of this study are available in the Article and its Supplementary Information.  Source data are provided with this paper.

Code availability

Pseudocode for the learning-in-simulation algorithm and training process can be found in the GitHub repository


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We thank Y. K. Chen at the Massachusetts Institute of Technology for constructive feedback and discussion of this work. This work was supported in part by the National Science Foundation (NSF) CAREER award CMMI 1944655, National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR) DRRP 90DPGE0019, Switzer Research Distinguished Fellow (SFGE22000372), NSF Future of Work 2231419 and National Institute of Health (NIH) 1R01EB035404.

Author information

Authors and Affiliations



H.S. first proposed the research idea and approach of the paper and provided the project guidance with input from E.J.R., B.Z. and X.Z. S.L., M.J. and H.S. were responsible for the design of this experiment. The methodology, code implementation and development were conducted by S.L. on the basis of a framework created by S.L. and X.Z. The mechatronics design and fabrication of the exoskeleton were done by I.D.S., M.J., S.Z. and J.Z. The experiments and data analysis were conducted by S.L., M.J., S.Z. and J.Z. The first draft of the manuscript was prepared by S.L., M.J., J.Z., S.Y., I.D.S., T.W. and H.S. Important revisions were made by S.L., J.Z., T.W., E.J.R., B.Z., H.Y., X.Z. and H.S. to the final paper. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Hao Su.

Ethics declarations

Competing interests

S.L. and H.S. are co-inventors on intellectual property related to the controller discussed in this work. H.S. is a co-founder of and has a financial interest in Picasso Intelligence, LLC. The terms of this arrangement have been reviewed and approved by NC State University in accordance with its policy on objectivity in research. The remaining authors declare no competing interests.

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Nature thanks Joonho Lee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Overview of the learning-in-simulation control framework.

a,b, Schematic illustrations for the learning-in-simulation architecture (a) and the control structure for online deployment (b).

Extended Data Fig. 2 Motion imitation neural network for versatile activities.

Schematic illustrations for the autonomous learning framework of the reference motions (walking, running, stair climbing) from datasets based on human kinematics input and joint torque command output.

Extended Data Fig. 3 Muscle coordination neural network.

Schematic illustrations for the muscle coordination neural network based on human joint torque input and human muscle actuation output.

Extended Data Fig. 4 Exoskeleton control neural network.

Schematic illustrations for the exoskeleton control neural network based on exoskeleton state history input and joint torque command output.

Extended Data Fig. 5 Exoskeleton design.

ac, Overall view of the whole system (a), actuator (b) and electronics (c).

Extended Data Fig. 6 Experiment protocol for metabolic rate and kinematic data collection during walking, running and stair climbing (also available on Protocol Exchange at:


Extended Data Table 1 Participant information
Extended Data Table 2 Summary of experiments

Supplementary information

Supplementary Information

Supplementary Text 1–7, Methods, Figs. 1–7, Tables 1–8 and references.

Supplementary Video 1

We present a control approach that learns assistive control strategies in simulation and can be transferred to a physical wearable robot to generate continuous assistance for several locomotion activities, including walking, running and stair climbing.

Supplementary Video 2

Human response to robots is slow and controller development typically requires 24–60 min human testing and is limited to single activity (mostly walking) control only. Our method leverages dynamics-aware and data-driven simulation. It requires no human testing and can be immediately deployed to a physical exoskeleton for several activities. We only need simulation once for 8 h to learn the assistive control policy that is transferred to microcontrollers for real-time control of physical exoskeletons.

Supplementary Video 3

The learned controller in simulation is transferred to a physical exoskeleton for real-time control that immediately improves mobility. Using control policies trained in simulation, the controller is versatile to assist several locomotion modes and leads to significant metabolic expenditure savings by 24.3%, 13.1% and 15.4% during walking, running and stair climbing, respectively, compared with no exoskeleton conditions.

Supplementary Video 4

The robot learns control strategies by simultaneous training of muscle-coordination neural network and robot controller neural network. It also learns multilocomotion control by an activity imitation neural network. We bridge the sim-to-real gap by domain randomization of muscle models and robot parameters and the control policy only requires one wearable sensor per leg to control the exoskeleton.

Supplementary Video 5

The robot donning takes about 2 min and doffing takes less than 1 min. The 3.2 kg low-profile hip exoskeleton does not affect the natural range of motion and thus can assist various movements for heterogeneous able-bodied individuals.

Source data

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Luo, S., Jiang, M., Zhang, S. et al. Experiment-free exoskeleton assistance via learning in simulation. Nature 630, 353–359 (2024).

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