Goku: Flow Based Video Generative Foundation Models
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Adina Yakefu
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ฯ0 and ฯ0-FAST: Vision-Language-Action Models for General Robot Control
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๐ Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! ๐ Preprint: https://arxiv.org/pdf/2409.19471
We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. ๐ค
๐กSELP presents three key insights:
1๏ธโฃ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications.
2๏ธโฃ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL.
3๏ธโฃ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency.
๐ Experiment: Our experiments demonstrate SELPโs effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate.
@yiwu @jiang719
#ICRA2025 #LLM #Robotics #Agent #LLMPlanner
We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. ๐ค
๐กSELP presents three key insights:
1๏ธโฃ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications.
2๏ธโฃ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL.
3๏ธโฃ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency.
๐ Experiment: Our experiments demonstrate SELPโs effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate.
@yiwu @jiang719
#ICRA2025 #LLM #Robotics #Agent #LLMPlanner
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Xwen ๐ฅ a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
โจ 7B/72B
โจ Apache 2.0
โจ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
โจ 7B/72B
โจ Apache 2.0
โจ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
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Xwen ๐ฅ a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
โจ 7B/72B
โจ Apache 2.0
โจ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
โจ 7B/72B
โจ Apache 2.0
โจ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
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Open-source DeepResearch โ Freeing our search agents
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Introducing ๐ผ๐ฝ๐ฒ๐ป ๐๐ฒ๐ฒ๐ฝ-๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต by Hugging Face! ๐ฅ
OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.
โฑ๏ธ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! โฑ๏ธ
โก๏ธ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...
We aimed for the best performance: are the agent's answers really rigorous?
On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
โก๏ธ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution ๐ช๐ช
And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !
Read the blog post ๐ https://huggingface.co/blog/open-deep-research
OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.
โฑ๏ธ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! โฑ๏ธ
โก๏ธ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...
We aimed for the best performance: are the agent's answers really rigorous?
On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
โก๏ธ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution ๐ช๐ช
And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !
Read the blog post ๐ https://huggingface.co/blog/open-deep-research
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๐ Introducing
@huggingface
Open Deep-Research๐ฅ
In just 24 hours, we built an open-source agent that:
โ Autonomously browse the web
โ Search, scroll & extract info
โ Download & manipulate files
โ Run calculations on data
55% on GAIA validation set! Help us improve it!๐ก
https://huggingface.co/blog/open-deep-research
In just 24 hours, we built an open-source agent that:
โ Autonomously browse the web
โ Search, scroll & extract info
โ Download & manipulate files
โ Run calculations on data
55% on GAIA validation set! Help us improve it!๐ก
https://huggingface.co/blog/open-deep-research
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Hey everyone, we've given https://hf.co/spaces page a fresh update!
Smart Search: Now just type what you want to doโlike "make a viral meme" or "generate music"โand our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
Weโd love to hear what you thinkโdrop us some feedback plz!
Smart Search: Now just type what you want to doโlike "make a viral meme" or "generate music"โand our search gets it.
New Categories: Check out the cool new filter bar with icons to help you pick a category fast.
Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.
Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.
Weโd love to hear what you thinkโdrop us some feedback plz!
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We just released the [BiRefNet_HR](
ZhengPeng7/BiRefNet_HR) for general use on higher resolution images, which was trained with images in
HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
2048x2048. If your images are mostly larger than 1024x1024, use BiRefNet_HR for better results! Thanks to
@Freepik
for the kind support of H200s for this huge training.HF Model: ZhengPeng7/BiRefNet_HR.
HF Demo: ZhengPeng7/BiRefNet_demo, where you need to choose General-HR and set high resolution.
PyTorch weights & ONNX: in Google Drive and the GitHub release.
Here is a comparison between the results of the original one and the new HR one on HR inputs:
And, the performance of this new HR one and the previous one trained in
1024x1024 on val set: