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Can vision-language-action (VLA) models generalize to diverse OOD tasks and align with customized objectives? 🤔 🚀 We introduce GRAPE, a plug-and-play algorithm to generalize robot policies via preference alignment. GRAPE unfolds three benefits to boost the generalizability of VLAs: 👉1. GRAPE aligns VLAs on a trajectory level and endows... the model with the ability for global decision-making, instead of merely cloning behavior; 👉2. GRAPE implicitly models reward from both successful and failed trials to boost generalizability to diverse tasks; 👉3. GRAPE adopts a scalable preference synthesis algorithm to rank trajectories with preferences that align with arbitrary objectives. Our experiments on a diverse array of real-world and simulated robotic tasks reveal: 1⃣GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 60.36%; 2⃣GRAPE is versatile to be aligned with diverse objectives and reduce collision rates by 44.31% or rollout length by 11.15% when aligning towards safer or more efficient manipulation policy, respectively. Check out our full project for more details: 🔥 Paper: 🔥 Project: 🔥 Code:show more

Huaxiu Yao

6,763 subscribers

20,071 views • 1 year ago •via X (Twitter)

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7 Comments

Huaxiu Yao1 year ago

[2/N] Detailed Method 1️⃣ Trajectory-wise Preference Optimization: GRAPE scales up step-wise VLAs and trains with a trajectory-wise objective, aligning policies globally by learning from both successes and failures. 2️⃣Customized Preference Synthesis: GRAPE breaks down complex tasks into stages, guided by spatiotemporal constraints from VL models. Flexibly aligns for arbitrary objectives, such as safety, efficiency, or task success. 3️⃣ Iterative Online Alignment: GRAPE refines the alignment process through iterative cycles of 1) online sample collection, 2) synthetic preference ranking, and 3) trajectory-wise preference optimization.

Huaxiu Yao1 year ago

[3/N] Empirical Takeaway 1: Stronger generalizability on a wide array of OOD tasks. 1️⃣ Real-world OOD tasks GRAPE crushes OpenVLA-SFT in generalization: - Visual (new visual environments) 🌆: +20.7% - Subject (unseen objects) 🔍: +27.5% - Action (unseen actions)🏃: +10.0% - Semantic (unseen prompts)🧠: +5.0% - Language grounding (objects in unseen spatial positions)🌍: +26.7% 2️⃣ Simulation OOD tasks In Simpler-Env, GRAPE shines: - Subject (unseen objects) 🔍: +8.0% - Physical (unseen object sizes/shapes) 🏗️: +12.3% - Semantic (unseen prompts)🧠: +19.0%

Huaxiu Yao1 year ago

[4/N] Empirical Takeaway 2: Versatility to align towards customized alignment objectives. GRAPE excels at aligning robot policies with diverse natural language goals: ✅ Task completion ✅ Safety ✅ Cost-efficiency Results: - 🚧 Safer policies: -44.31% collisions - ⏳ Efficient policies: -11.15% rollout lengths

Huaxiu Yao1 year ago

[5/N] Nice work, @ZijianZhangNLP , Kyle Zheng, and nice collab. w/ @ZRChen_AISafety , @jang_yoel , @Yi_Li_UW , @chaoqi_w , @dingmyu , @fox_dieter17849

ZijianZhangNLP1 year ago

Cool work！Thank Prof. Yao and our nice collab!

zihao zheng1 year ago

what is the required training resources？

Daniel Butler1 year ago

GRAPE seems like a promising leap for VLA models in robotics! The trajectory-level preference alignment and reward modeling are particularly intriguing for enabling safer, more efficient, and task-diverse applications. How scalable is GRAPE to real-world multi-agent environments?

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61,026 views • 8 months ago

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Pretraining is essential for good performance on a wide variety of robotics tasks, and so most vision-language-action models build off of a vision language model (VLM) trained on a wide variety of image-language data. But how does the choice of VLM translate to downstream robotics performance? Jianke Zhang and @GYanjiang join us to talk about this key part of the robot policy, looking at a wide variety of different VLMs and how they perform. Interestingly, they see that performance on auxiliary tasks like quesiton answering did not lead to downstream improvements in control. To learn more, watch episode 65 of RoboPapers now, with Chris Paxton and Jiafei Duan!

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23,905 views • 6 months ago

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Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

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343,919 views • 2 years ago

Alert🚨 This could be by far the most impressive video about 4D human motion generation! 🔥🔥🔥 We present TRUMANS, a pioneering and solid effort that scales up 4D human-scene interactions with both static and dynamic objects. With our model, we could eventually generate diverse and physically plausible human motions of any length in the physical 3D world. Cannot stop to wonder how TRUMANS could be adapted to the humanoid robot. Let me know if you are interested! Our paper has been accepted by CVPR24; check our code, demo, and more details in

Alert🚨 This could be by far the most impressive video about 4D human motion generation! 🔥🔥🔥 We present TRUMANS, a pioneering and solid effort that scales up 4D human-scene interactions with both static and dynamic objects. With our model, we could eventually generate diverse and physically plausible human motions of any length in the physical 3D world. Cannot stop to wonder how TRUMANS could be adapted to the humanoid robot. Let me know if you are interested! Our paper has been accepted by CVPR24; check our code, demo, and more details in

Siyuan Huang

13,689 views • 2 years ago

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models paper page: github: Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.

AK

83,681 views • 3 years ago

A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)

A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)

Chris Paxton

20,486 views • 1 year ago

Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️

Working on multimodal instruction tuning and finding it hard to scale? Building Web/GUI agents but data is too narrow? Introducing 🚀MultiUI: 7.3M multimodal instructions from 1M webpage UIs, offering diverse data to boost text-rich visual understanding. Key takeaways: 🌟WebUI-trained models show major gains in visual web understanding and agent tasks. 💻 🌟Models also generalize well to non-UI tasks like DocVQA/OCR. 📄 How it works: We generate multimodal instructions with a text LLM using structured text from webpage accessibility trees. We then pair them with UI screenshots, to train multimodal models. Homepage: Paper: Dataset: Model: Congrats to the student lead Junpeng Liu and the team Tianyue Ou Yifan Song Yuxiao Qu Chenyan Xiong Wenhu Chen Graham Neubig ! More details are in the following threads ⬇️

Xiang Yue

57,736 views • 1 year ago