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Johnny Chung Lee

Johnny Chung Lee

I joined Google in 2011, and I am currently an Engineering Director leading a robotics effort in Google's machine learning division. My areas of interest include machine learning, computer vision, sensors, robotics, and human-computer interaction. In my career, I've had the opportunity to work on the following projects:
  • Founded and lead Google Tango in 2013, a mobile computer vision effort to bring 6-DOF tracking and 3D sensing to mobile phones. This included the development and launch of multiple mobile phones incorporating 3D sensing and tracking. The technology from Tango has become ARCore for Android enabling mobile AR applications, and the Visual Positioning Service for Google Maps.
  • Lead the software team that developed 6-DOF tracking for Daydream VR headsets.
  • Oversaw some hardware R&D efforts for VR/AR technologies, including novel tracking technologies, headset prototypes, optical technologies, and sensing algorithms.
  • As a part of Google X, I made early contributions to Google Glass, Loon, and other non-public projects.
  • Prior to Google, I developed human tracking algorithms for the Xbox 360 Kinect at Microsoft.
  • Personal projects have included low-cost interactive whiteboards and desktop VR displays using the Nintendo Wii remote, which have received over 15 million views, performed as a TED talk in 2008, and recognized by MIT Technology Review's TR35.
I enjoy developing hardware and software systems that enhance human-interaction with computing systems. I received a Ph.D. in Human-Computer Interaction from Carnegie Mellon University with Scott Hudson in 2008. I received a B.S. in Computer Engineering from the University of Virginia in 2001 where I worked on manipulating perceptual psychology with VR with Dennis Proffit.
Authored Publications
Google Publications
Other Publications
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    Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming
    Zhongyi Zhou
    Jing Jin
    Xiuxiu Yuan
    Jun Jiang
    Jingtao Zhou
    Yiyi Huang
    Kristen Wright
    Jason Mayes
    Mark Sherwood
    Ram Iyengar
    Na Li
    Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 5 (to appear)
    Preview abstract Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation. View details
    InstructPipe: Building Visual Programming Pipelines with Human Instructions
    Zhongyi Zhou
    Jing Jin
    Xiuxiu Yuan
    Jun Jiang
    Jingtao Zhou
    Yiyi Huang
    Kristen Wright
    Jason Mayes
    Mark Sherwood
    Ram Iyengar
    Na Li
    arXiv, vol. 2312.09672 (2023)
    Preview abstract Visual programming provides beginner-level programmers with a coding-free experience to build their customized pipelines. Existing systems require users to build a pipeline entirely from scratch, implying that novice users need to set up and link appropriate nodes all by themselves, starting from a blank workspace. We present InstructPipe, an AI assistant that enables users to start prototyping machine learning (ML) pipelines with text instructions. We designed two LLM modules and a code interpreter to execute our solution. LLM modules generate pseudocode of a target pipeline, and the interpreter renders a pipeline in the node-graph editor for further human-AI collaboration. Technical evaluations reveal that InstructPipe reduces user interactions by 81.1% compared to traditional methods. Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands. View details
    Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
    Ryan Hoque
    Kaushik Shivakumar
    Shrey Aeron
    Gabriel Deza
    Aditya Ganapathi
    Andy Zeng
    Ken Goldberg
    IEEE International Conference on Intelligent Robots and Systems (IROS) (2022) (to appear)
    Preview abstract Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a new cloud robotics platform that enables low-latency remote execution of control policies on physical robots, we present the first systematic benchmarking of fabric manipulation algorithms on physical hardware. We develop 4 novel learning-based algorithms that model expert actions, keypoints, reward functions, and dynamic motions, and we compare these against 4 learning-free and inverse dynamics algorithms on the task of folding a crumpled T-shirt with a single robot arm. The entire lifecycle of data collection, model training, and policy evaluation is performed remotely without physical access to the robot workcell. Results suggest a new algorithm combining imitation learning with analytic methods achieves 84% of human-level performance on the folding task. View details
    Preview abstract Large pretrained (e.g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g., spreadsheets, SAT questions, code). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this diversity is symbiotic, and can be leveraged through Socratic Models (SMs): a modular framework in which multiple pretrained models may be composed zero-shot i.e., via multimodal-informed prompting, to exchange information with each other and capture new multimodal capabilities, without requiring finetuning. With minimal engineering, SMs are not only competitive with state-of-the-art zero-shot image captioning and video-to-text retrieval, but also enable new applications such as (i) answering free-form questions about egocentric video, (ii) engaging in multimodal assistive dialogue with people (e.g., for cooking recipes) by interfacing with external APIs and databases (e.g., web search), and (iii) robot perception and planning. Prototypes are available at socraticmodels.github.io View details
    Implicit Behavioral Cloning
    Pete Florence
    Corey Lynch
    Andy Zeng
    Oscar Ramirez
    Laura Downs
    Igor Mordatch
    CoRL (2021)
    Preview abstract We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions. On robotic policy learning tasks we show that implicit behavioral cloning policies with energy-based models (EBM) often outperform common explicit (Mean Square Error, or Mixture Density) behavioral cloning policies, including on tasks with high-dimensional action spaces and visual image inputs. We find these policies provide competitive results or outperform state-of-the-art offline reinforcement learning methods on the challenging human-expert tasks from the D4RL benchmark suite, despite using no reward information. In the real world, robots with implicit policies can learn complex and remarkably subtle behaviors on contact-rich tasks from human demonstrations, including tasks with high combinatorial complexity and tasks requiring 1mm precision. View details
    ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
    Shreeyak Sajjan
    Matthew Moore
    Mike Pan
    Ganesh Nagaraja
    Andy Zeng
    Shuran Song
    IEEE International Conference on Robotics and Automation (ICRA) (2020)
    Preview abstract Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp – a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to real-world images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms' performance on transparent objects. Code, data, benchmarks, and supplementary materials are available at: https://sites.google.com/view/cleargrasp View details
    Preview abstract Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high degrees of freedom (DoF) and dynamically react to the environment. However, due to the challenges of collecting effective training data and learning efficiently, most grasping algorithms today are limited to top-down movements and open-loop execution. In this work, we propose a new low-cost hardware interface for collecting grasping demonstrations by people in diverse environments. Leveraging this data, we show that it is possible to train a robust end-to-end 6DoF closed-loop grasping model with reinforcement learning that transfers to real robots. A key aspect of our grasping model is that it uses “action-view” based rendering to simulate future states with respect to different possible actions. By evaluating these states using a learned value function (Q-function), our method is able to better select corresponding actions that maximize total rewards (i.e., grasping success). Our final grasping system is able to achieve reliable 6DoF closed-loop grasping of novel objects across various scene configurations, as well as dynamic scenes with moving objects. View details
    Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
    Kevin Zakka
    Andy Zeng
    Shuran Song
    IEEE International Conference on Robotics and Automation (ICRA) (2020)
    Preview abstract Is it possible to learn policies for robotic assembly that can generalize to new objects? We explore this idea in the context of the kit assembly task. Since classic methods rely heavily on object pose estimation, they often struggle to generalize to new objects without 3D CAD models or task-specific training data. In this work, we propose to formulate the kit assembly task as a shape matching problem, where the goal is to learn a shape descriptor that establishes geometric correspondences between object surfaces and their target placement locations from visual input. This formulation enables the model to acquire a broader understanding of how shapes and surfaces fit together for assembly – allowing it to generalize to new objects and kits. To obtain training data for our model, we present a self-supervised data-collection pipeline that obtains ground truth object-to-placement correspondences by disassembling complete kits. Our resulting real-world system, Form2Fit, learns effective pick and place strategies for assembling objects into a variety of kits – achieving 90% average success rates under different initial conditions (e.g. varying object and kit poses), 94% success under new configurations of multiple kits, and over 86% success with completely new objects and kits. View details
    Transporter Networks: Rearranging the Visual World for Robotic Manipulation
    Andy Zeng
    Pete Florence
    Stefan Welker
    Jonathan Chien
    Travis Armstrong
    Ivan Krasin
    Dan Duong
    Conference on Robot Learning (CoRL) (2020)
    Preview abstract Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass object(s) or an end effector. In this work, we propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from visual input -- which can parameterize robot actions. It makes no assumptions of objectness (e.g. canonical poses, models, or keypoints), it exploits spatial symmetries, and is orders of magnitude more sample efficient than our benchmarked alternatives in learning vision-based manipulation tasks: from stacking a pyramid of blocks, to assembling kits with unseen objects; from manipulating deformable ropes, to pushing piles of small objects with closed-loop feedback. Our method can represent complex multi-modal policy distributions and generalizes to multi-step sequential tasks, as well as 6DoF pick-and-place. Experiments on 10 simulated tasks show that it learns faster and generalizes better than a variety of end-to-end baselines, including policies that use ground-truth object poses. We validate our methods with hardware in the real world. View details
    Spatial Action Maps for Mobile Manipulation
    Jimmy Wu
    Xingyuan Sun
    Andy Zeng
    Shuran Song
    Szymon Rusinkiewicz
    Robotics: Science and Systems (RSS) (2020)
    Preview abstract Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from images of the current state (e.g., a bird's-eye view of a SLAM reconstruction). Instead, we show that it can be advantageous to learn with dense action representations defined in the same domain as the state. In this work, we present "spatial action maps," in which the set of possible actions is represented by a pixel map (aligned with the input image of the current state), where each pixel represents a local navigational endpoint at the corresponding scene location. Using ConvNets to infer spatial action maps from state images, action predictions are thereby spatially anchored on local visual features in the scene, enabling significantly faster learning of complex behaviors for mobile manipulation tasks with reinforcement learning. In our experiments, we task a robot with pushing objects to a goal location, and find that policies learned with spatial action maps achieve much better performance than traditional alternatives. View details
    TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
    Andy Zeng
    Shuran Song
    Alberto Rodriguez
    Robotics: Science and Systems (RSS) (2019)
    Preview abstract Throwing is a means to increase the capabilities of a manipulator by exploiting dynamics, a form of dynamic extrinsic dexterity. In the case of pick-and-place for example, throwing can enable a robot arm to rapidly place objects into selected boxes outside its maximum kinematic range — improving its physical reachability and picking speed. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. grasp of the object) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e. using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and successfully throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. View details
    Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
    Andy Zeng
    Shuran Song
    Stefan Welker
    Alberto Rodriguez
    IEEE International Conference on Intelligent Robots and Systems (IROS) (2018)
    Preview abstract Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch by combining visual affordance-based manipulation with model-free deep reinforcement learning. Our method is sample efficient and generalizes to novel objects and scenarios. View details
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