Robotic arm choreography often reproduces trajectories while missing cultural semantics. This study examines whether symbolic posture transfer with joint space compatible notation can preserve semantic fidelity on a six-degree-of-freedom arm and remain portable across morphologies. We implement ROPERA, a three-stage pipeline for encoding culturally codified postures, composing symbolic sequences, and decoding to servo commands. A scene from Kunqu opera, \textit{The Peony Pavilion}, serves as the material for evaluation. The procedure includes corpus-based posture selection, symbolic scoring, direct joint angle execution, and a visual layer with light painting and costume-informed colors. Results indicate reproducible execution with intended timing and cultural legibility reported by experts and audiences. The study points to non-anthropocentric cultural preservation and portable authoring workflows. Future work will design dance-informed transition profiles, extend the notation to locomotion with haptic, musical, and spatial cues, and test portability across platforms.

Interfaces for contemporary large language, generative media, and perception AI models are often engineered for single user interaction. We investigate ritual as a design scaffold for developing collaborative, multi-user human-AI engagement. We consider the specific case of an immersive staging of the musical Xanadu performed at UCLA in Spring 2025. During a two-week run, over five hundred audience members contributed sketches and jazzercise moves that vision language models translated to virtual scenery elements and from choreographic prompts. This paper discusses four facets of interaction-as-ritual within the show: audience input as offerings that AI transforms into components of the ritual; performers as ritual guides, demonstrating how to interact with technology and sorting audience members into cohorts; AI systems as instruments "played" by the humans, in which sensing, generative components, and stagecraft create systems that can be mastered over time; and reciprocity of interaction, in which the show's AI machinery guides human behavior as well as being guided by humans, completing a human-AI feedback loop that visibly reshapes the virtual world. Ritual served as a frame for integrating linear narrative, character identity, music and interaction. The production explored how AI systems can support group creativity and play, addressing a critical gap in prevailing single user AI design paradigms.

We describe DANCE^2, an interactive dance performance in which audience members channel their collective agency into a dancer-robot duet by voting on the behavior of a wearable robot affixed to the dancer's body. At key moments during the performance, the audience is invited to either continue the choreography or override it, shaping the unfolding interaction through real-time collective input. While post-performance surveys revealed that participants felt their choices meaningfully influenced the performance, voting data across four public performances exhibited strikingly consistent patterns. This tension between what audience members do, what they feel, and what actually changes highlights a complex interplay between agentive behavior, the experience of agency, and power. We reflect on how choreography, interaction design, and the structure of the performance mediate this relationship, offering a live analogy for algorithmically curated digital systems where agency is felt, but not exercised.

Dancers often prototype movements themselves or with each other during improvisation and choreography. How are these interactions altered when physically manipulable technologies are introduced into the creative process? To understand how dancers design and improvise movements while working with instruments capable of non-humanoid movements, we engaged dancers in workshops to co-create movements with a robot arm in one-human-to-one-robot and three-human-to-one-robot settings. We found that dancers produced more fluid movements in one-to-one scenarios, experiencing a stronger sense of connection and presence with the robot as a co-dancer. In three-to-one scenarios, the dancers divided their attention between the human dancers and the robot, resulting in increased perceived use of space and more stop-and-go movements, perceiving the robot as part of the stage background. This work highlights how technologies can drive creativity in movement artists adapting to new ways of working with physical instruments, contributing design insights supporting artistic collaborations with non-humanoid agents.

We introduce MusicInfuser, an approach for generating high-quality dance videos that are synchronized to a specified music track. Rather than attempting to design and train a new multimodal audio-video model, we show how existing video diffusion models can be adapted to align with musical inputs by introducing lightweight music-video cross-attention and a low-rank adapter. Unlike prior work requiring motion capture data, our approach fine-tunes only on dance videos. MusicInfuser achieves high-quality music-driven video generation while preserving the flexibility and generative capabilities of the underlying models. We introduce an evaluation framework using Video-LLMs to assess multiple dimensions of dance generation quality. The project page and code are available at https://susunghong.github.io/MusicInfuser.

Existing AI-generated dance methods primarily train on motion capture data from solo dance performances, but a critical feature of dance in nearly any genre is the interaction of two or more bodies in space. Moreover, many works at the intersection of AI and dance fail to incorporate the ideas and needs of the artists themselves into their development process, yielding models that produce far more useful insights for the AI community than for the dance community. This work addresses both needs of the field by proposing an AI method to model the complex interactions between pairs of dancers and detailing how the technical methodology can be shaped by ongoing co-creation with the artistic stakeholders who curated the movement data. Our model is a probability-and-attention-based Variational Autoencoder that generates a choreographic partner conditioned on an input dance sequence. We construct a custom loss function to enhance the smoothness and coherence of the generated choreography. Our code is open-source, and we also document strategies for other interdisciplinary research teams to facilitate collaboration and strong communication between artists and technologists.

Catalyzed by advancements in hardware and software, drone performances are increasingly making their mark in the entertainment industry. However, designing smooth and safe choreographies for drone swarms is complex and often requires expert domain knowledge. In this work, we introduce SwarmGPT-Primitive, a language-based choreographer that integrates the reasoning capabilities of large language models (LLMs) with safe motion planning to facilitate deployable drone swarm choreographies. The LLM composes choreographies for a given piece of music by utilizing a library of motion primitives; the language-based choreographer is augmented with an optimization-based safety filter, which certifies the choreography for real-world deployment by making minimal adjustments when feasibility and safety constraints are violated. The overall SwarmGPT-Primitive framework decouples choreographic design from safe motion planning, which allows non-expert users to re-prompt and refine compositions without concerns about compliance with constraints such as avoiding collisions or downwash effects or satisfying actuation limits. We demonstrate our approach through simulations and experiments with swarms of up to 20 drones performing choreographies designed based on various songs, highlighting the system's ability to generate effective and synchronized drone choreographies for real-world deployment.

'I think AI's going to change everything," Tamara Rojo, artistic director of San Francisco Ballet, told me earlier this year. "We just don't know quite how." The impact of artificial intelligence on the creative industries can already be seen across film, television and music, but to some extent dance seems insulated, as a form that so much relies on live bodies performing in front of an audience. But this week choreographers Aoi Nakamura and Esteban Lecoq, collectively known as AΦE, are launching what is billed as the world's first AI-driven dance production, Lilith.Aeon. Lilith, the performer, is an AI entity, who has co-created the work, with Nakamura and Lecoq. "She" will appear on an LED cube that the audience move around, their motion triggering Lilith's dance. Nakamura and Lecoq insist they're interested not in chasing the latest technology for its own sake but in enhancing their storytelling. Working as dancers with theatre company Punchdrunk turned them on to the idea of ...

Renée DiResta is the research manager of the Stanford Internet Observatory and the author of Invisible Rulers: The People Who Turn Lies into Reality. At the end of May, OpenAI marked a new "first" in its corporate history. It wasn't an even more powerful language model or a new data partnership, but a report disclosing that bad actors had misused their products to run influence operations. The company had caught five networks of covert propagandists--including players from Russia, China, Iran, and Israel--using their generative AI tools for deceptive tactics that ranged from creating large volumes of social media comments in multiple languages to turning news articles into Facebook posts. The use of these tools, OpenAI noted, seemed intended to improve the quality and quantity of output.

Synthesising appropriate choreographies from music remains an open problem. We introduce MDLT, a novel approach that frames the choreography generation problem as a translation task. Our method leverages an existing data set to learn to translate sequences of audio into corresponding dance poses. We present two variants of MDLT: one utilising the Transformer architecture and the other employing the Mamba architecture. We train our method on AIST++ and PhantomDance data sets to teach a robotic arm to dance, but our method can be applied to a full humanoid robot. Evaluation metrics, including Average Joint Error and Frechet Inception Distance, consistently demonstrate that, when given a piece of music, MDLT excels at producing realistic and high-quality choreography. The code can be found at github.com/meowatthemoon/MDLT.