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Introduce CoT-VLA – Visual Chain-of-Thought reasoning for Robot Foundation Models! 🤖 By leveraging next-frame prediction as visual chain-of-thought reasoning, CoT-VLA uses future prediction to guide action generation and unlock large-scale video data for training. #CVPR2025

Qingqing Zhao

2,596 subscribers

48,381 views • 1 year ago •via X (Twitter)

Science & Technology #CVPR2025

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Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. Paper Title: MindDrive: A Vision-Language-Action Model for Autonomous Driving via Project: Link:

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🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

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LeRobot

115,194 views • 9 months ago

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Google Research

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𝗜'𝘃𝗲 𝗵𝗲𝗮𝗿𝗱 𝘁𝗵𝗶𝘀 𝗮 𝗹𝗼𝘁 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆: "𝗪𝗲 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝘂𝗿 𝗿𝗼𝗯𝗼𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗼𝗯𝗷𝗲𝗰𝘁 𝗮𝗻𝗱 𝗶𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗲𝗱 𝘁𝗼 𝗮 𝗻𝗼𝘃𝗲𝗹 𝗼𝗯𝗷𝗲𝗰𝘁 - 𝘁𝗵𝗲𝘀𝗲 𝗻𝗲𝘄 𝗩𝗟𝗔 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗰𝗿𝗮𝘇𝘆!" Let's talk about what's actually happening in that "A" (Action) part of your VLA model. The Vision and Language components? They're incredible. Pre-trained on internet-scale data, they understand objects, spatial relationships, and task instructions better than ever. But the Action component? That's still learned from scratch on your specific robot demonstrations. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: Your VLA model has internet-scale understanding of what a screwdriver looks like and what "tighten the screw" means. But the actual motor pattern for "rotating wrist while applying downward pressure"? That comes from your 500 robot demos. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 "𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻": • 𝗩𝗶𝘀𝗶𝗼𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻: Recognises novel objects instantly (thanks to pre-training) • 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻: Understands new task instructions (thanks to pre-training) • 𝗔𝗰𝘁𝗶𝗼𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻: Still limited to motor patterns seen during robot training Ask that same robot to "unscrew the bottle cap" and it fails because: • Vision: Recognises bottle and cap • Language: Understands "unscrew" • Action: Never learned the "twist while pulling" motor pattern 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵 𝗮𝗯𝗼𝘂𝘁 𝗩𝗟𝗔 𝗺𝗼𝗱𝗲𝗹𝘀: The "VL" gives you incredible zero-shot understanding. The "A" still requires task-specific demonstrations. We've cracked the perception and reasoning problem. We haven't cracked the motor generalisation problem.

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Collective agent compute, on-demand for your task. A swarm of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.

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124,048 views • 1 year ago

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25,279 views • 1 year ago

Announcing our $320M Series A at a $2.3B valuation, led by Khosla Ventures, with General Catalyst, Eric Schmidt and Jeff Bezos. General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.

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General Intuition

520,318 views • 1 month ago

GPT Image 2 + Seedance 2.0 - Prompt Share Created on @MartiniArt_ Created the character in Midjourney, then built the storyboard around that character. This time I used a more detailed video prompt, but I wasn't describing the sequence shot-by-shot. The storyboard acted more as a visual guide for the action flow, camera language and overall narrative than as a strict frame-by-frame blueprint. Looking at the result, the model seems to preserve the key beats, escalation and composition of the storyboard while freely interpreting the transitions between them.

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Kōda

41,565 views • 2 months ago

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

Chubby♨️

21,151 views • 9 months ago

fastUSD has officially launched on Sei, bringing a new era of liquidity to the fastest blockchain. Powered by Elixir, fastUSD is a yield-bearing stablecoin integrated with DRAGONDEX, jellyverse, to start. These integrations set the foundation for a wide range of DeFi applications, offering greater composability and liquidity provisioning for Sei’s DEXs. Cross-chain capabilities are enabled through Hyperlane ⏩, ensuring seamless asset transfers, while RedStone ♦️ oracle secures data feeds, enhancing fastUSD’s reliability and security. As the next-generation stable asset for Sei, fastUSD is poised to play a pivotal role in the continued growth of DeFi within the ecosystem.

fastUSD has officially launched on Sei, bringing a new era of liquidity to the fastest blockchain. Powered by Elixir, fastUSD is a yield-bearing stablecoin integrated with DRAGONDEX, jellyverse, to start. These integrations set the foundation for a wide range of DeFi applications, offering greater composability and liquidity provisioning for Sei’s DEXs. Cross-chain capabilities are enabled through Hyperlane ⏩, ensuring seamless asset transfers, while RedStone ♦️ oracle secures data feeds, enhancing fastUSD’s reliability and security. As the next-generation stable asset for Sei, fastUSD is poised to play a pivotal role in the continued growth of DeFi within the ecosystem.

Sei

116,165 views • 1 year ago

I am thrilled to join Rhoda AI as an advisor, where I am helping harness the abilities of large-scale pre-training and video models for robotics, putting many of my lab's research learnings of the past few years into practice! I will be in-person at the Mountain View office for part of the summer - reach out if you want to chat :)

I am thrilled to join Rhoda AI as an advisor, where I am helping harness the abilities of large-scale pre-training and video models for robotics, putting many of my lab's research learnings of the past few years into practice! I will be in-person at the Mountain View office for part of the summer - reach out if you want to chat :)

Vincent Sitzmann

54,699 views • 1 month ago

30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:

30 minutes of video. Robot learns the task. Open-source, end-to-end. An open-source framework for training robot policies from only 30 minutes of human egocentric videos captured via Meta Aria glasses: Achieving zero-shot transfer to robots without any robot data collection. The method relies on Interaction-Centric Tokens that encode hand-object spatial relationships invariant to embodiment and viewpoint, supplemented by auxiliary objectives like object motion prediction and latent consistency to extract richer supervision signals from the same data. HumanEgo demonstrates strong cross-embodiment, cross-environment performance on bimanual tasks, outperforming baselines like ACT and teleop data while being trainable on a single RTX 4090 GPU. Thanks for sharing, Zhi (Leo) Wang. 📌 Website: Paper: Code: Video: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

17,077 views • 2 months ago

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.

Tokenization is quickly becoming one of the most talked-about ideas in crypto. This year we’re aiming much higher. We don’t just want to tokenize miner revenue; we want to bring entire productive ecosystems on-chain. That means tokenizing baskets of assets, real energy infrastructure, AI agents, compute power, data centres, and even the cooling systems that keep high-performance hardware running at scale. All of it connected and interoperable with DeFi. Thats what we have been building throughout. A single on-chain framework that unlocks something genuinely powerful: real ownership, real yield, and real coordination between digital and physical infrastructure. From backend systems and front-end interfaces to smart contracts, AI, physical infrastructure, oracles, and autonomous agents, the stack keeps expanding and compounding. It’s about building the foundation for how value will be owned, deployed, and earned in the next generation of the on-chain economy. Simplified by PinLink.

PinLink

14,339 views • 6 months ago

Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain

Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain

HYPE3.cool

17,700 views • 1 year ago