ArticleNature machine intelligence2019
Autonomous Functional Movements in a Tendon-Driven Limb via Limited Experience.
Article in Nature machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 40 citations in OpenAlex.
- Curriculum is more influential than haptic feedback when learning object manipulation.Science advances · 2025Article
- Brain-body-task co-adaptation can improve autonomous learning and speed of bipedal walking.Bioinspiration & biomimetics · 2024Article
- Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- A computational study of how an α- to γ-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An alpha- to gamma-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement: A computational study.bioRxiv : the preprint server for biology · 2024Article
- Neuromuscular control: from a biomechanist's perspective.Frontiers in sports and active living · 2023Review
- Edge Computing in Nature: Minimal pre-processing of multi-muscle ensembles of spindle signals improves discriminability of limb movements.Frontiers in physiology · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Robots will become ubiquitously useful only when they can use few attempts to teach themselves to perform different tasks, even with complex bodies and in dynamical environments. Vertebrates, in fact, use sparse trial-and-error to learn multiple tasks despite their intricate tendon-driven anatomies-which are particularly hard to control because they are simultaneously nonlinear, under-determined, and over-determined. We demonstrate-for the first time in simulation and hardware-how a model-free, open-loop approach allows few-shot autonomous learning to produce effective movements in a 3-tendon 2-joint limb. We use a short period of motor babbling (to create an initial inverse map) followed by building functional habits by reinforcing high-reward behavior and refinements of the inverse map in a movement's neighborhood. This biologically-plausible algorithm, which we call G2P (General-to-Particular), can potentially enable quick, robust and versatile adaptation in robots as well as shed light on the foundations of the enviable functional versatility of organisms.
Identifiers
What OpenQuestion holds
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.