RoboTales: ROBOTic Anthropomorphic LEarning Systems
- URL: http://arxiv.org/abs/2606.26213v1
- Date: Wed, 24 Jun 2026 17:41:02 GMT
- Title: RoboTales: ROBOTic Anthropomorphic LEarning Systems
- Abstract summary: RoboTales is a low-cost robotic storytelling system that animates narratives using expressive sock puppetry.<n>It synchronizes narration, gestures, and mouth movements to perform character-driven stories.<n>In a pilot study, puppet-based storytelling outperformed a gesture-only mode, producing higher HRIES ratings and improved story recall.
- Score: 0.18501087920760131
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: RoboTales is a low-cost robotic storytelling system that animates narratives using expressive sock puppetry. Implemented autonomously on a Baxter robot as a test case, RoboTales synchronizes narration, gestures, and mouth movements to perform character-driven stories. In a pilot study, puppet-based storytelling outperformed a gesture-only mode, producing higher HRIES ratings and improved story recall, suggesting that embodied puppetry enhances engagement and narrative comprehension. Designed to be modular and platform-agnostic, RoboTales can be adapted to other manipulators and offers a screen-free alternative to passive media, supporting future deployment in child-centered learning environments.
Related papers
- Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data [60.94418315274988]
Ego2Robot supports both curated and in-the-wild videos, producing 18,561 hours of robot training data spanning 15 robot morphologies.<n>Joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple types.
arXiv Detail & Related papers (2026-08-03T17:52:26Z) - LLM-Powered Interactive Robotic Action Synthesis from Multimodal Speech, Gestures, and Music [1.7188280334580195]
This paper introduces a novel framework that synthesizes complex robotic actions from a rich tapestry of multimodal human inputs.<n>Our system architecture integrates a speech transcription model, a gesture recognition module, and a signal processing pipeline for beat detection.<n>The framework has ability to interpret and fuse semantic commands from speech, deictic information from gestures, and rhythmic cues from music.
arXiv Detail & Related papers (2026-06-30T05:35:26Z) - BotDirector: Robot Storytelling Across the Symmetrical Reality with Multi-modal Interactions [25.188575753340416]
We propose an interactive system that facilitates robot storytelling with tangible and natural language interactions.<n>Children arrange the playground with their own stuff and create narratives with an LLM agent.<n>The created narratives are transformed into a motion sequence based on the map and characters, and the motions are executed by self-navigating swarm robots.
arXiv Detail & Related papers (2026-06-02T06:33:35Z) - Aesthetics of Robot-Mediated Applied Drama: A Case Study on REMind [3.8229109732721778]
Social robots are increasingly used in education, but most applications cast them as tutors offering explanation-based instruction.<n>We explore an alternative: Robot-Mediated Applied Drama (RMAD), in which robots function as life-like puppets in interactive dramatic experiences.<n>This paper presents REMind, an anti-bullying robot role-play game that helps children rehearse bystander intervention and peer support.
arXiv Detail & Related papers (2026-03-25T00:58:01Z) - Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control [52.83779852397341]
Current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands.<n>We propose RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio.<n>RoboPerform achieves a ResMoE policy for adapting to diverse motion patterns and a diffusion-based student policy for audio style injection.
arXiv Detail & Related papers (2025-12-29T17:59:24Z) - H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos [58.006918399913665]
We propose a video-to-video translation framework that converts ordinary human-object interaction videos into motion-consistent robot manipulation videos.<n>Our approach does not require any paired human-robot videos for training only a set of unpaired robot videos, making the system easy to scale.<n>At test time, we apply the same process to human videos (inpainting the person and overlaying human pose cues) and generate high-quality robot videos that mimic the human's actions.
arXiv Detail & Related papers (2025-12-10T07:59:45Z) - VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation [53.63540587160549]
VidBot is a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos.<n> VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.
arXiv Detail & Related papers (2025-03-10T10:04:58Z) - Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videos [101.26467307473638]
We introduce Moto, which converts video content into latent Motion Token sequences by a Latent Motion Tokenizer.<n>We pre-train Moto-GPT through motion token autoregression, enabling it to capture diverse visual motion knowledge.<n>To transfer learned motion priors to real robot actions, we implement a co-fine-tuning strategy that seamlessly bridges latent motion token prediction and real robot control.
arXiv Detail & Related papers (2024-12-05T18:57:04Z) - RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation [33.10577695383743]
We propose a multi-embodiment, multi-task generalist agent for robotic manipulation called RoboCat.
This data spans a large repertoire of motor control skills from simulated and real robotic arms with varying sets of observations and actions.
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples.
arXiv Detail & Related papers (2023-06-20T17:35:20Z) - Synthesis and Execution of Communicative Robotic Movements with
Generative Adversarial Networks [59.098560311521034]
We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects.
We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics.
We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
arXiv Detail & Related papers (2022-03-29T15:03:05Z) - Caption Generation of Robot Behaviors based on Unsupervised Learning of
Action Segments [10.356412004005767]
Bridging robot action sequences and their natural language captions is an important task to increase explainability of human assisting robots.
In this paper, we propose a system for generating natural language captions that describe behaviors of human assisting robots.
arXiv Detail & Related papers (2020-03-23T03:44:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.