ArticleiScience2025
Emergence of natural and robust bipedal walking by learning from biologically plausible objectives.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- [Progress and application of machine learning in sports injury research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Bioinspired morphology and task curricula for learning locomotion in bipedal muscle-actuated systems.Communications engineering · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans show unparalleled ability when maneuvering diverse terrains. While reinforcement learning (RL) has shown great promise for musculoskeletal simulation in the development of robust controllers, complex behaviors are only achievable under extensive use of motion data. We demonstrate that the combination of a recent RL algorithm with a biologically plausible reward is capable of learning controllers for 4 different musculoskeletal models and achieves locomotion with up to 90 muscles without demonstrations. Our controllers generalize to diverse and unseen terrains, while only a single adaptive objective function is needed for training. We validate our findings on four models in two different simulators. The RL agents perform robustly with complex 3D models, where reflex-controllers are difficult to apply, and produce close-to-natural motion. This is a first step for the motor control, biomechanics, and rehabilitation communities to generate complex human movements with RL, without using motion data or simple unrepresentative models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.