Ryo Suzuki

Ryo Suzuki, Adnan Karim, Tian Xia et al.

This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics.

Takami Sato, Yuki Hayakawa, Ryo Suzuki et al.

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research finds that one can manipulate the LiDAR point cloud and fool object detectors by firing malicious lasers against LiDAR. However, these efforts face 3 critical research gaps: (1) considering only one specific LiDAR (VLP-16); (2) assuming unvalidated attack capabilities; and (3) evaluating object detectors with limited spoofing capability modeling and setup diversity. To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets. To facilitate the measurements, we (1) identify spoofer improvements that significantly improve the latest spoofing capability, (2) identify a new object removal attack that overcomes the applicability limitation of the latest method to new-generation LiDARs, and (3) perform novel mathematical modeling for both object injection and removal attacks based on our measurement results. Through this study, we are able to uncover a total of 15 novel findings, including not only completely new ones due to the measurement angle novelty, but also many that can directly challenge the latest understandings in this problem space. We also discuss defenses.

Jian Liao, Adnan Karim, Shivesh Jadon et al.

We present RealityTalk, a system that augments real-time live presentations with speech-driven interactive virtual elements. Augmented presentations leverage embedded visuals and animation for engaging and expressive storytelling. However, existing tools for live presentations often lack interactivity and improvisation, while creating such effects in video editing tools require significant time and expertise. RealityTalk enables users to create live augmented presentations with real-time speech-driven interactions. The user can interactively prompt, move, and manipulate graphical elements through real-time speech and supporting modalities. Based on our analysis of 177 existing video-edited augmented presentations, we propose a novel set of interaction techniques and then incorporated them into RealityTalk. We evaluate our tool from a presenter's perspective to demonstrate the effectiveness of our system.

Kyzyl Monteiro, Ritik Vatsal, Neil Chulpongsatorn et al.

This paper introduces Teachable Reality, an augmented reality (AR) prototyping tool for creating interactive tangible AR applications with arbitrary everyday objects. Teachable Reality leverages vision-based interactive machine teaching (e.g., Teachable Machine), which captures real-world interactions for AR prototyping. It identifies the user-defined tangible and gestural interactions using an on-demand computer vision model. Based on this, the user can easily create functional AR prototypes without programming, enabled by a trigger-action authoring interface. Therefore, our approach allows the flexibility, customizability, and generalizability of tangible AR applications that can address the limitation of current marker-based approaches. We explore the design space and demonstrate various AR prototypes, which include tangible and deformable interfaces, context-aware assistants, and body-driven AR applications. The results of our user study and expert interviews confirm that our approach can lower the barrier to creating functional AR prototypes while also allowing flexible and general-purpose prototyping experiences.

Matthew Brehmer, Maxime Cordeil, Christophe Hurter et al.

We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

Neil Chulpongsatorn, Mille Skovhus Lunding, Nishan Soni et al.

We introduce Augmented Math, a machine learning-based approach to authoring AR explorable explanations by augmenting static math textbooks without programming. To augment a static document, our system first extracts mathematical formulas and figures from a given document using optical character recognition (OCR) and computer vision. By binding and manipulating these extracted contents, the user can see the interactive animation overlaid onto the document through mobile AR interfaces. This empowers non-technical users, such as teachers or students, to transform existing math textbooks and handouts into on-demand and personalized explorable explanations. To design our system, we first analyzed existing explorable math explanations to identify common design strategies. Based on the findings, we developed a set of augmentation techniques that can be automatically generated based on the extracted content, which are 1) dynamic values, 2) interactive figures, 3) relationship highlights, 4) concrete examples, and 5) step-by-step hints. To evaluate our system, we conduct two user studies: preliminary user testing and expert interviews. The study results confirm that our system allows more engaging experiences for learning math concepts.

Wanli Qian, Chenfeng Gao, Anup Sathya et al.

This paper introduces text-to-shape-display, a novel approach to generating dynamic shape changes in pin-based shape displays through natural language commands. By leveraging large language models (LLMs) and AI-chaining, our approach allows users to author shape-changing behaviors on demand through text prompts without programming. We describe the foundational aspects necessary for such a system, including the identification of key generative elements (primitive, animation, and interaction) and design requirements to enhance user interaction, based on formative exploration and iterative design processes. Based on these insights, we develop SHAPE-IT, an LLM-based authoring tool for a 24 x 24 shape display, which translates the user's textual command into executable code and allows for quick exploration through a web-based control interface. We evaluate the effectiveness of SHAPE-IT in two ways: 1) performance evaluation and 2) user evaluation (N= 10). The study conclusions highlight the ability to facilitate rapid ideation of a wide range of shape-changing behaviors with AI. However, the findings also expose accuracy-related challenges and limitations, prompting further exploration into refining the framework for leveraging AI to better suit the unique requirements of shape-changing systems.

Ryo Suzuki, Yutaro Yamaguchi

In the sliding window model, we are required to maintain the target statistics over the most recent $n$ elements of a data stream, which is captured by a window of size $n$ sliding over the data stream. Exact computation usually requires space linear in $n$, and the central goal is approximate maintenance using sublinear space. In this paper, we study the problem of maintaining the maximum subarray sum in the sliding window model. While the classical Kadane's algorithm computes the exact answer using constant space in the static setting, it does not extend directly, because a new element makes the oldest one expire, which may invalidate the optimal subarray so far. Our first observation is that the so-called Smooth Histogram framework can lead to a constant-factor approximation (in the sense of relative error) using $O((\log n)^2)$ bits of space. We then refine this framework accordingly, which enables for any $ε> 0$ to maintain a $(1 \pm ε)$-approximation using $O(ε^{-1}(\log n)^2)$ bits of space and $O(ε^{-1}\log n)$ operations per update. The space complexity is asymptotically optimal.

Xiaoan Liu, DaeHo Lee, Eric J Gonzalez et al.

We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution. Running on Meta Ray-Ban smart glasses, VisionClaw continuously perceives real-world context and enables in-situ, speech-driven action initiation and delegation via OpenClaw AI agents. Therefore, users can directly execute tasks through the smart glasses, such as adding real-world objects to an Amazon cart, generating notes from physical documents, receiving meeting briefings on the go, creating events from posters, or controlling IoT devices. We evaluate VisionClaw through a controlled laboratory study (N=12) and a longitudinal deployment study (N=5). Results show that integrating perception and execution enables faster task completion and reduces interaction overhead compared to non-always-on and non-agent baselines. Beyond performance gains, deployment findings reveal a shift in interaction: tasks are initiated opportunistically during ongoing activities, and execution is increasingly delegated rather than manually controlled. These results suggest a new paradigm for wearable AI agents, where perception and action are continuously coupled to support situated, hands-free interaction.

Xiaoan Liu, Eric J Gonzalez, Nels Numan et al.

Bridging the physical and digital world through interaction remains a core challenge in augmented reality (AR). Existing systems target single objects, limiting support for planning, comparison, and assembly tasks that depend on relationships among multiple items. We present Semantic Reality, an AR system focused on surfacing inter-object connectivity and making it interactive. Leveraging multimodal reasoning, spatial anchoring, and physical action recognition, Semantic Reality maintains a persistent model of objects around the user and their relationships. Connections are visualized in-situ to highlight compatibility, reveal next steps, and reduce ambiguity during tasks. We contribute a connectivity-centered interaction paradigm and a system architecture that couples anchor tracking, action sensing, and model inference to construct a live connectivity graph. In an exploratory study comparing Semantic Reality to a single-object baseline, participants reported clearer inter-object understanding and higher engagement and satisfaction, without increased workload. A scenario study illustrates where connectivity aids planning, sequencing, and disambiguation.

Ryo Suzuki, Shohei Watabe

Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method offers a useful alternative to Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA), its implementation on Noisy Intermediate-Scale Quantum (NISQ) devices is severely limited by the gate counts and depth of manually designed ansatz. Here, we present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN). Our approach treats circuit construction as a multi-objective optimization problem, simultaneously minimizing energy expectation values and optimizing circuit complexity. By introducing adoptive thresholds, we demonstrate significant hardware overhead reductions. In Max-Cut problems, our agent autonomously discovered circuits with approximately 37\% fewer gates and 43\% less depth than standard hardware-efficient ansatz on average. For molecular hydrogen ($H_2$), the DDQN also achieved the Full-CI limit, with maintaining a significantly shallower circuit. These results suggest that deep reinforcement learning can be helpful to find non-intuitive, optimal circuit structures, providing a pathway toward efficient, hardware-aware quantum algorithm design.

Reudismam Rolim, Gustavo Soares, Loris D'Antoni et al.

IDEs, such as Visual Studio, automate common transformations, such as Rename and Extract Method refactorings. However, extending these catalogs of transformations is complex and time-consuming. A similar phenomenon appears in intelligent tutoring systems where instructors have to write cumbersome code transformations that describe "common faults" to fix similar student submissions to programming assignments. We present REFAZER, a technique for automatically generating program transformations. REFAZER builds on the observation that code edits performed by developers can be used as examples for learning transformations. Example edits may share the same structure but involve different variables and subexpressions, which must be generalized in a transformation at the right level of abstraction. To learn transformations, REFAZER leverages state-of-the-art programming-by-example methodology using the following key components: (a) a novel domain-specific language (DSL) for describing program transformations, (b) domain-specific deductive algorithms for synthesizing transformations in the DSL, and (c) functions for ranking the synthesized transformations. We instantiate and evaluate REFAZER in two domains. First, given examples of edits used by students to fix incorrect programming assignment submissions, we learn transformations that can fix other students' submissions with similar faults. In our evaluation conducted on 4 programming tasks performed by 720 students, our technique helped to fix incorrect submissions for 87% of the students. In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code. In our evaluation conducted on 59 scenarios of repetitive edits taken from 3 C# open-source projects, REFAZER learns the intended program transformation in 83% of the cases.

Keiichi Ihara, Tianle Li, Yasuhisa Shiino et al.

We present MemoryDiorama, a prototype system that introduces augmented memory cues, a concept that extends captured personal media with AI-generated contextual information to enhance autobiographical memory recall. MemoryDiorama transforms everyday photos into dynamic 3D dioramas in mixed reality by integrating LLM-based scene analysis with 3D object generation, animation, and spatial composition. The system extracts geographic information, object attributes, lighting conditions, and atmospheric elements from the photos. It then animates these elements with generative components such as object animations, human motion, geographical effects, and particle effects to provide richer cues for memory recall. We evaluated MemoryDiorama in a within-subject user study with 18 participants, comparing three conditions: Photo-Only, Static Diorama, and MemoryDiorama. Compared with both Photo-Only and Static Diorama, MemoryDiorama elicited more internal and in-cue details during recall. It also increased perceptual details and visual vividness ratings, suggesting richer recollective experience.

Ryo Suzuki, Mar Gonzalez-Franco, Misha Sra et al.

This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences. With recent advancements in both AR hardware and AI capabilities, we envision that everyday AR -- always-available and seamlessly integrated into users' daily environments -- is becoming increasingly feasible. This workshop will explore how AI can drive such everyday AR experiences. We discuss a range of topics, including adaptive and context-aware AR, generative AR content creation, always-on AI assistants, AI-driven accessible design, and real-world-oriented AI agents. Our goal is to identify the opportunities and challenges in AI-enabled AR, focusing on creating novel AR experiences that seamlessly blend the digital and physical worlds. Through the workshop, we aim to foster collaboration, inspire future research, and build a community to advance the research field of AI-enhanced AR.

Keiichi Ihara, DaeHo Lee, Manato Abe et al.

We introduce CinemaWorld, a generative augmented reality system that augments the viewer's physical surroundings with automatically generated mixed reality 3D content extracted from and synchronized with 2D movie scenes. Our system preprocesses films to extract key features using multimodal large language models (LLMs), generates dynamic 3D augmentations with generative AI, and embeds them spatially into the viewer's physical environment on the Meta Quest 3. To explore the design space of CinemaWorld, we conducted an elicitation study with eight film students, which led us to identify several key augmentation types, including particle effects, surrounding objects, textural overlays, character-driven augmentation, and lighting effects. We evaluated our system through a technical evaluation (N=100 video clips), a user study (N=12), and expert interviews with film creators (N=8). Results indicate that CinemaWorld enhances immersion and enjoyment, suggesting its potential to enrich the film-viewing experience.

Aditya Gunturu, Ben Pearman, Keiichi Ihara et al.

We introduce MapStory, an LLM-powered animation prototyping tool that generates editable map animation sequences directly from natural language text by leveraging a dual-agent LLM architecture. Given a user written script, MapStory automatically produces a scene breakdown, which decomposes the text into key map animation primitives such as camera movements, visual highlights, and animated elements. Our system includes a researcher agent that accurately queries geospatial information by leveraging an LLM with web search, enabling automatic extraction of relevant regions, paths, and coordinates while allowing users to edit and query for changes or additional information to refine the results. Additionally, users can fine-tune parameters of these primitive blocks through an interactive timeline editor. We detail the system's design and architecture, informed by formative interviews with professional animators and by an analysis of 200 existing map animation videos. Our evaluation, which includes expert interviews (N=5) and a usability study (N=12), demonstrates that MapStory enables users to create map animations with ease, facilitates faster iteration, encourages creative exploration, and lowers barriers to creating map-centric stories.

Samin Farajian, Hiroki Kaimoto, Ryo Suzuki

We introduce Swarm Fabrication, a novel concept of creating on-demand, scalable, and reconfigurable fabrication machines made of swarm robots. We present ways to construct an element of fabrication machines, such as motors, elevator, table, feeder, and extruder, by leveraging toio robots and 3D printed attachments. By combining these elements, we demonstrate constructing a X-Y-Z plotter with multiple toio robots, which can be used for drawing plotters and 3D printers. We also show the possibility to extend our idea to more general-purpose fabrication machines, which include 3D printers, CNC machining, foam cutters, line drawing devices, pick and place machines, 3D scanning, etc. Through this, we draw a future vision, where the swarm robots can construct a scalable and reconfigurable fabrication machines on-demand, which can be deployed anywhere the user wishes. We believe this fabrication technique will become a means of interactive and highly flexible fabrication in the future.

Ryo Suzuki, Eyal Ofek, Mike Sinclair et al.

HapticBots introduces a novel encountered-type haptic approach for Virtual Reality (VR) based on multiple tabletop-size shape-changing robots. These robots move on a tabletop and change their height and orientation to haptically render various surfaces and objects on-demand. Compared to previous encountered-type haptic approaches like shape displays or robotic arms, our proposed approach has an advantage in deployability, scalability, and generalizability -- these robots can be easily deployed due to their compact form factor. They can support multiple concurrent touch points in a large area thanks to the distributed nature of the robots. We propose and evaluate a novel set of interactions enabled by these robots which include: 1) rendering haptics for VR objects by providing just-in-time touch-points on the user's hand, 2) simulating continuous surfaces with the concurrent height and position change, and 3) enabling the user to pick up and move VR objects through graspable proxy objects. Finally, we demonstrate HapticBots with various applications, including remote collaboration, education and training, design and 3D modeling, and gaming and entertainment.

Ryo Suzuki, Hooman Hedayati, Clement Zheng et al.

RoomShift is a room-scale dynamic haptic environment for virtual reality, using a small swarm of robots that can move furniture. RoomShift consists of nine shape-changing robots: Roombas with mechanical scissor lifts. These robots drive beneath a piece of furniture to lift, move and place it. By augmenting virtual scenes with physical objects, users can sit on, lean against, place and otherwise interact with furniture with their whole body; just as in the real world. When the virtual scene changes or users navigate within it, the swarm of robots dynamically reconfigures the physical environment to match the virtual content. We describe the hardware and software implementation, applications in virtual tours and architectural design and interaction techniques.

Ryo Suzuki, Rubaiat Habib Kazi, Li-Yi Wei et al.

We present RealitySketch, an augmented reality interface for sketching interactive graphics and visualizations. In recent years, an increasing number of AR sketching tools enable users to draw and embed sketches in the real world. However, with the current tools, sketched contents are inherently static, floating in mid air without responding to the real world. This paper introduces a new way to embed dynamic and responsive graphics in the real world. In RealitySketch, the user draws graphical elements on a mobile AR screen and binds them with physical objects in real-time and improvisational ways, so that the sketched elements dynamically move with the corresponding physical motion. The user can also quickly visualize and analyze real-world phenomena through responsive graph plots or interactive visualizations. This paper contributes to a set of interaction techniques that enable capturing, parameterizing, and visualizing real-world motion without pre-defined programs and configurations. Finally, we demonstrate our tool with several application scenarios, including physics education, sports training, and in-situ tangible interfaces.

Ryo Suzuki, Jun Kato, Koji Yatani

Programming education is becoming important as demands on computer literacy and coding skills are growing. Despite the increasing popularity of interactive online learning systems, many programming courses in schools have not changed their teaching format from the conventional classroom setting. We see two research opportunities here. Students may have diverse expertise and experience in programming. Thus, particular content and teaching speed can be disengaging for experienced students or discouraging for novice learners. In a large classroom, instructors cannot oversee the learning progress of each student, and have difficulty matching teaching materials with the comprehension level of individual students. We present ClassCode, a web-based environment tailored to programming education in classrooms. Students can take online tutorials prepared by instructors at their own pace. They can then deepen their understandings by performing interactive coding exercises interleaved within tutorials. ClassCode tracks all interactions by each student, and summarizes them to instructors. This serves as a progress report, facilitating the instructors to provide additional explanations in-situ or revise course materials. Our user evaluation through a small lecture and expert review by instructors and teaching assistants confirm the potential of ClassCode by uncovering how it could address issues in existing programming courses at universities.

Ryo Suzuki, Ryosuke Nakayama, Dan Liu et al.

Large-scale shape-changing interfaces have great potential, but creating such systems requires substantial time, cost, space, and efforts, which hinders the research community to explore interactions beyond the scale of human hands. We introduce modular inflatable actuators as building blocks for prototyping room-scale shape-changing interfaces. Each actuator can change its height from 15cm to 150cm, actuated and controlled by air pressure. Each unit is low-cost (8 USD), lightweight (10 kg), compact (15 cm), and robust, making it well-suited for prototyping room-scale shape transformations. Moreover, our modular and reconfigurable design allows researchers and designers to quickly construct different geometries and to explore various applications. This paper contributes to the design and implementation of highly extendable inflatable actuators, and demonstrates a range of scenarios that can leverage this modular building block.

Ryo Suzuki, Clement Zheng, Yasuaki Kakehi et al.

We introduce shape-changing swarm robots. A swarm of self-transformable robots can both individually and collectively change their configuration to display information, actuate objects, act as tangible controllers, visualize data, and provide physical affordances. ShapeBots is a concept prototype of shape-changing swarm robots. Each robot can change its shape by leveraging small linear actuators that are thin (2.5 cm) and highly extendable (up to 20cm) in both horizontal and vertical directions. The modular design of each actuator enables various shapes and geometries of self-transformation. We illustrate potential application scenarios and discuss how this type of interface opens up possibilities for the future of ubiquitous and distributed shape-changing interfaces.

Ryo Suzuki, Koji Yatani, Mark D. Gross et al.

This paper presents Tabby, an interactive and explorable design tool for 3D printing textures. Tabby allows texture design with direct manipulation in the following workflow: 1) select a target surface, 2) sketch and manipulate a texture with 2D drawings, and then 3) generate 3D printing textures onto an arbitrary curved surface. To enable efficient texture creation, Tabby leverages an auto-completion approach which automates the tedious, repetitive process of applying texture, while allowing flexible customization. Our user evaluation study with seven participants confirms that Tabby can effectively support the design exploration of different patterns for both novice and experienced users.

Ryo Suzuki, Gustavo Soares, Andrew Head et al.

Recent advances in program synthesis offer means to automatically debug student submissions and generate personalized feedback in massive programming classrooms. When automatically generating feedback for programming assignments, a key challenge is designing pedagogically useful hints that are as effective as the manual feedback given by teachers. Through an analysis of teachers' hint-giving practices in 132 online Q&A posts, we establish three design guidelines that an effective feedback design should follow. Based on these guidelines, we develop a feedback system that leverages both program synthesis and visualization techniques. Our system compares the dynamic code execution of both incorrect and fixed code and highlights how the error leads to a difference in behavior and where the incorrect code trace diverges from the expected solution. Results from our study suggest that our system enables students to detect and fix bugs that are not caught by students using another existing visual debugging tool.

Ryo Suzuki, Abigale Stangl, Mark D. Gross et al.

For people with visual impairments, tactile graphics are an important means to learn and explore information. However, raised line tactile graphics created with traditional materials such as embossing are static. While available refreshable displays can dynamically change the content, they are still too expensive for many users, and are limited in size. These factors limit wide-spread adoption and the representation of large graphics or data sets. In this paper, we present FluxMaker, an inexpensive scalable system that renders dynamic information on top of static tactile graphics with movable tactile markers. These dynamic tactile markers can be easily reconfigured and used to annotate static raised line tactile graphics, including maps, graphs, and diagrams. We developed a hardware prototype that actuates magnetic tactile markers driven by low-cost and scalable electromagnetic coil arrays, which can be fabricated with standard printed circuit board manufacturing. We evaluate our prototype with six participants with visual impairments and found positive results across four application areas: location finding or navigating on tactile maps, data analysis, and physicalization, feature identification for tactile graphics, and drawing support. The user study confirms advantages in application domains such as education and data exploration.

Ryo Suzuki, Tom Yeh, Koji Yatani et al.

Texture is an essential property of physical objects that affects aesthetics, usability, and functionality. However, designing and applying textures to 3D objects with existing tools remains difficult and time-consuming; it requires proficient 3D modeling skills. To address this, we investigated an auto-completion approach for efficient texture creation that automates the tedious, repetitive process of applying texture while allowing flexible customization. We developed techniques for users to select a target surface, sketch and manipulate a texture with 2D drawings, and then generate 3D printable textures onto an arbitrary curved surface. In a controlled experiment our tool sped texture creation by 80% over conventional tools, a performance gain that is higher with more complex target surfaces. This result confirms that auto-completion is powerful for creating 3D textures.

Ryo Suzuki, Niloufar Salehi, Michelle S. Lam et al.

Expert crowdsourcing marketplaces have untapped potential to empower workers' career and skill development. Currently, many workers cannot afford to invest the time and sacrifice the earnings required to learn a new skill, and a lack of experience makes it difficult to get job offers even if they do. In this paper, we seek to lower the threshold to skill development by repurposing existing tasks on the marketplace as mentored, paid, real-world work experiences, which we refer to as micro-internships. We instantiate this idea in Atelier, a micro-internship platform that connects crowd interns with crowd mentors. Atelier guides mentor-intern pairs to break down expert crowdsourcing tasks into milestones, review intermediate output, and problem-solve together. We conducted a field experiment comparing Atelier's mentorship model to a non-mentored alternative on a real-world programming crowdsourcing task, finding that Atelier helped interns maintain forward progress and absorb best practices.