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Centaur

2020
Royal Danish Theatre / Théâtre National de Chaillot, Paris / Teatros del Canal, Madrid
2020 – 2022
Centaur

Nine dancers taught the machine to move — then had to share the stage with what it remembered.

Centaur is a full-length contemporary dance piece in which a machine learning system, trained on the movement of the dancers themselves, becomes an active presence in the performance. Developed by choreographer Pontus Lidberg with composer Ryoji Ikeda, the work unfolds as a live negotiation between human bodies and the model built from them.

We needed to capture high-resolution movement data from every rehearsal. To do this we used the Sewio RTLS ultra-wideband tracking system, with sensors worn by each dancer streaming positional data in real time. A backend received that stream and uploaded it to BigQuery, where it was stored and labelled for use as training data.

Two parallel approaches were developed for generating new choreography from that data. The primary model was an LSTM with a Mixture Density Network output layer, trained to produce movement sequences from the distribution of what the dancers had already done. A genetic algorithm ran as a second compositional engine. A batch generation pipeline orchestrated both approaches. Coordinating audio timing, assembling image sequences into video, and packaging outputs. Running hundreds of choreographic variants across the studio cluster over many hours.

Live on stage, the system runs as three services communicating over OSC. The scoring service ingests the real-time sensor stream, compares it against precomputed reference files that define what each passage of the dance should look like, runs a PyTorch model to calculate dancer scores, and broadcasts those scores to the lighting and visual systems. All within the latency budget of a live performance. The stage machine sits on the local theatre network and is reachable remotely over WireGuard when the show is on tour.

Shown at the Royal Danish Theatre, Copenhagen; Théâtre National de Chaillot, Paris; and Teatros del Canal, Madrid, 2020 – 2022.

Thanks to: Augustinusfonden, Overretssagfører L. Zeuthens Mindelegat, William Demant Fonden, Knud Højgaards Fond, Beckett-Fonden, Jyllands-Postens Fond

Tech

machine learning dance performance LSTM reinforcement learning real-time

Venue

Royal Danish Theatre / Théâtre National de Chaillot, Paris / Teatros del Canal, Madrid

2020 – 2022

Team

Pontus Lidberg
Choreography
Cecilie Waagner Falkenstrøm
AI Installation Artist
Ryoji Ikeda
Audio & Visual Design
Tomonaga Tokuyama
Computer Programming for Visuals
Raphael Frisenvænge Solholm
Light Design
Rachel Quarmby-Spadaccini
Costume Design
Adrian Guo Silver
Dramaturge

Gallery

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