Stanford Vision and Learning Lab @StanfordSVL
SVL is led by @drfeifei @silviocinguetta @jcniebles @jiajunwu_cs and works on machine learning, computer vision, robotics and language svl.stanford.edu Stanford, CA Joined September 2014-
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Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced
spent some time this weekend reading some of the papers that people have been theorizing underly Sora -- first cool concept from them, joint image & video training from @agrimgupta92
Our research introduces a system that enables you to generate 3D environments from text prompts and train embodied AI agents within them! Website: yueyang1996.github.io/holodeck/ Code: github.com/allenai/Holode… How did we leverage Objaverse assets to create interactive 3D environments? 👇
Can we give LLMs access to a visual scratchpad with diagrammatic abstractions and improve reasoning on text-based tasks? Come to the (spoiler) I Can't Believe It's Not Better workshop at @NeurIPSConf on Saturday to find out!
3D Copy-Paste: seamlessly copy virtual objects and paste them into real scenes, maintaining physically plausible integration. This generated data enhances monocular 3D detection models, achieving State-of-the-Art performance. #NeurIPS2023 🌐 gyhandy.github.io/3D-Copy-Paste/
We introduce W.A.L.T, a diffusion model for photorealistic video generation. Our model is a transformer trained on image and video generation in a shared latent space. 🧵👇
Color, material, category… —visual concepts characterize different aspects of visual entities. We introduce a framework to recognize these language-informed concepts from images and recompose them to generate new images, e.g., “a blue 🟦 metallic 🪙 Teddy Bear 🧸”.
Can generative AI imagine what Alice saw in her journey in the Wonderland 🏞️🚶♀️? Introducing WonderJourney: Create a journey (a long sequence of diverse yet connected 3D scenes) from a single image or text! 🧵1/N Web: kovenyu.com/wonderjourney/ arxiv: arxiv.org/abs/2312.03884
Excited to talk about Logic-Enhanced Foundation Models (LEFT) @NeurIPSConf next week! Come chat with us on Tuesday morning at #203. Try out our Colab notebook to train your own LEFT and learn concepts on a new dataset in ~100 lines of code. 🔗Colab: colab.research.google.com/drive/1PHHvjIm…
Excited to talk about Logic-Enhanced Foundation Models (LEFT) @NeurIPSConf next week! Come chat with us on Tuesday morning at #203. Try out our Colab notebook to train your own LEFT and learn concepts on a new dataset in ~100 lines of code. 🔗Colab: colab.research.google.com/drive/1PHHvjIm…
Does GPT-4V understand geometric concepts as humans do? We revisit Geoclidean, and ask GPT-4V to learn geometric concepts from few examples. We see that GPT-4V's performance in classifying geometric abstractions differs significantly from that of humans.
Text-to-image models like DALL-E create stunning images. Their widespread use urges transparent evaluation of their capabilities and risks. 📣 We introduce HEIM: a benchmark for holistic evaluation of text-to-image models arxiv.org/abs/2311.04287 (in #NeurIPS2023 Datasets) [1/n]
Evaluation of modern generative models is challenging. Check out HEIM: amazing work led by @tonyh_lee @michiyasunaga @chenlin_meng. A new benchmark for evaluating text to image generation models 🧵👇
Evaluation of modern generative models is challenging. Check out HEIM: amazing work led by @tonyh_lee @michiyasunaga @chenlin_meng. A new benchmark for evaluating text to image generation models 🧵👇
Kosta Derpanis @CSProfKGD
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159K Followers 123 Following CEO & co-founder of @duolingo. Invented reCAPTCHA. MacArthur Fellow. Former computer science professor at Carnegie Mellon. Proud Guatemalan. @LvA_Foundation.We especially thank @kenny__shaw @anag004 @pathak2206 for open-sourcing the LEAP Hand project. Having a customizable and low-cost dexterous hand benefits our project a lot!
We hope DexCap paves the path for future research on scaling up robot data with wearable devices. The code, data, and hardware are open-sourced at 🌐dex-cap.github.io Work done w/ @HaochenShi74 @KenWangWeizhuo @RuohanZhang76 @drfeifei Karen Liu @StanfordAILab @StanfordSVL
Also check out the fun failure modes of our robot. 8/
However, DexCap is not yet ready for tasks that require applying force, as positional data alone is insufficient. Therefore, we introduce DexCap for human-in-the-loop correction during rollouts. Within 30 trials of corrections, our robot can prepare tea🍵 and use scissors✂️. 7/
DexCap enables fast data collection, approximating the speed of natural human motion. Moreover, the collection process does not require costly robot hardware. 6/
DexCap is fully portable and can scale up data collection in the wild. By collecting data with multiple objects in diverse environments, the learned policy can generalize to unseen objects for the same task. 5/
We train a point cloud-based Diffusion Policy with retargeted human mocap data only. The robot controls both hands (46-dim action space) to perform tasks including collecting tennis balls🎾 and packaging objects🎁. All the policies are learned without any teleoperation data. 4/
We then retarget the mocap data to the robot embodiment. This includes (1) Observation retargeting by switching the camera system from human to robot. (2) Action retargeting by matching fingertip positions with IK. (3) Bridging the visual gap by including robot point clouds. 3/
Motion capture gloves, unlike vision-based tracking, are not affected by occlusions during hand-object interactions, perfect for mocap in daily activities. With an RGB-D camera, DexCap reconstructs 3D scenes and aligns motion data, all powered by a mini-PC in the backpack. 2/
Can we use wearable devices to collect robot data without actual robots? Yes! With a pair of gloves🧤! Introducing DexCap, a portable hand motion capture system that collects 3D data (point cloud + finger motion) for training robots with dexterous hands Everything open-sourced
Our research introduces a system that enables you to generate 3D environments from text prompts and train embodied AI agents within them! Website: yueyang1996.github.io/holodeck/ Code: github.com/allenai/Holode… How did we leverage Objaverse assets to create interactive 3D environments? 👇
Can we give LLMs access to a visual scratchpad with diagrammatic abstractions and improve reasoning on text-based tasks? Come to the (spoiler) I Can't Believe It's Not Better workshop at @NeurIPSConf on Saturday to find out!
3D Copy-Paste: seamlessly copy virtual objects and paste them into real scenes, maintaining physically plausible integration. This generated data enhances monocular 3D detection models, achieving State-of-the-Art performance. #NeurIPS2023 🌐 gyhandy.github.io/3D-Copy-Paste/
We introduce W.A.L.T, a diffusion model for photorealistic video generation. Our model is a transformer trained on image and video generation in a shared latent space. 🧵👇
Color, material, category… —visual concepts characterize different aspects of visual entities. We introduce a framework to recognize these language-informed concepts from images and recompose them to generate new images, e.g., “a blue 🟦 metallic 🪙 Teddy Bear 🧸”.
Can generative AI imagine what Alice saw in her journey in the Wonderland 🏞️🚶♀️? Introducing WonderJourney: Create a journey (a long sequence of diverse yet connected 3D scenes) from a single image or text! 🧵1/N Web: kovenyu.com/wonderjourney/ arxiv: arxiv.org/abs/2312.03884
@NeurIPSConf And also check out our Colab demo to evaluate a trained LEFT on a human motion domain! 🔗Colab: colab.research.google.com/drive/1b0Bzlyr…
Excited to talk about Logic-Enhanced Foundation Models (LEFT) @NeurIPSConf next week! Come chat with us on Tuesday morning at #203. Try out our Colab notebook to train your own LEFT and learn concepts on a new dataset in ~100 lines of code. 🔗Colab: colab.research.google.com/drive/1PHHvjIm…
What’s left w/ foundation models? We found that they still can't ground modular concepts across domains. We present Logic-Enhanced FMs:🤝FMs & neuro-symbolic concept learners. We learn abstractions of concepts like “left” across domains & do domain-independent reasoning w/ LLMs.
Does GPT-4V understand geometric concepts as humans do? We revisit Geoclidean, and ask GPT-4V to learn geometric concepts from few examples. We see that GPT-4V's performance in classifying geometric abstractions differs significantly from that of humans.
A robot may be unable to complete a task when limited by its morphology. Remarkably, people and some animals can get around this by not only using but also *designing* tools. We explore whether robots can also do this in our latest work! 🌐robotic-tool-design.github.io 🧵👇
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