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Gemini Robotics 1.5 features a separate reasoning engine (ER), but its VLA model is also capable of thinking due to interleaved reasoning tokens. The VLA is able to independently operate long autonomous sequences (15+ minutes) without aid from the ER/VLM.

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The Humanoid Hub

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34,214 görüntüleme • 8 ay önce •via X (Twitter)

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Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.
1:42

Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.

The Humanoid Hub

65,928 görüntüleme • 9 ay önce

One core bottleneck in VLA models is action representation. Discrete tokens scale beautifully with VLM pretraining—but lose precision. Continuous actions are precise—but often break VLM reasoning. In our new work, we resolve this tension at the representation level. 🧵
0:41

One core bottleneck in VLA models is action representation. Discrete tokens scale beautifully with VLM pretraining—but lose precision. Continuous actions are precise—but often break VLM reasoning. In our new work, we resolve this tension at the representation level. 🧵

Jianlan Luo

47,993 görüntüleme • 6 ay önce

We’re launching the first in a series of Embodied Reasoning offerings: Perceptron Egocentric. It achieves SOTA over the best robotic annotation pipelines built on Gemini 3.5 Flash and Gemini Robotics-ER 1.6. Early access is available to partners.
0:58

We’re launching the first in a series of Embodied Reasoning offerings: Perceptron Egocentric. It achieves SOTA over the best robotic annotation pipelines built on Gemini 3.5 Flash and Gemini Robotics-ER 1.6. Early access is available to partners.

Perceptron AI

86,518 görüntüleme • 3 gün önce

What if robots could think longer on harder problems without saying a single word?🤔 We introduce RD-VLA (Recurrent-Depth VLA): a latent, iterative reasoning architecture for robot control. ❌No Chain-of-Thought tokens. ❌No extra memory overhead. ✅Just reasoning—directly in latent space. 🧠🤖 Project page: 👇🧵
1:22

What if robots could think longer on harder problems without saying a single word?🤔 We introduce RD-VLA (Recurrent-Depth VLA): a latent, iterative reasoning architecture for robot control. ❌No Chain-of-Thought tokens. ❌No extra memory overhead. ✅Just reasoning—directly in latent space. 🧠🤖 Project page: 👇🧵

Jiafei Duan

13,833 görüntüleme • 5 ay önce

For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:
1:39

For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:

Boston Dynamics

60,551 görüntüleme • 2 ay önce

Google DeepMind introduced two foundational models for embodied reasoning, enabling robots to comprehend, react, and take action in the physical world: ⦿ Gemini Robotics – built on Gemini 2.0. Integrates vision, language, and action for real-world dexterity, . ⦿ Gemini Robotics-ER – Enhances spatial reasoning for advanced robotic control. They are working with Apptronik to develop the next generation of humanoid robots.
1:11

Google DeepMind introduced two foundational models for embodied reasoning, enabling robots to comprehend, react, and take action in the physical world: ⦿ Gemini Robotics – built on Gemini 2.0. Integrates vision, language, and action for real-world dexterity, . ⦿ Gemini Robotics-ER – Enhances spatial reasoning for advanced robotic control. They are working with Apptronik to develop the next generation of humanoid robots.

The Humanoid Hub

73,097 görüntüleme • 1 yıl önce

Want to see Gemini 2.0 Flash Thinking in action? Check out this demo where the model solves a physics problem and explains its reasoning.
1:24

Want to see Gemini 2.0 Flash Thinking in action? Check out this demo where the model solves a physics problem and explains its reasoning.

Jeff Dean

105,766 görüntüleme • 1 yıl önce

We present VLM-3R: a Vision-Language Model capable of 3D spatial reasoning from monocular video, grounding visual cues, geometry, and camera motion. ✅ No depth sensor ✅ No pre-built 3D maps ✅ End-to-end spatial + temporal reasoning 🔗 Code & benchmark: #VLM #3DVision #LLMs
0:20

We present VLM-3R: a Vision-Language Model capable of 3D spatial reasoning from monocular video, grounding visual cues, geometry, and camera motion. ✅ No depth sensor ✅ No pre-built 3D maps ✅ End-to-end spatial + temporal reasoning 🔗 Code & benchmark: #VLM #3DVision #LLMs

Zhiwen(Aaron) Fan

14,895 görüntüleme • 1 yıl önce

Last month we announced Gemini Robotics (GR) and Gemini Robotics-ER (GR-ER). GR-ER is a powerful VLM specialised for spatial understanding, including detecting object poses in 2D/3D, pointing, and even *predicting grasp poses*. Take a look at this demo. Details below. 🧵
1:42

Last month we announced Gemini Robotics (GR) and Gemini Robotics-ER (GR-ER). GR-ER is a powerful VLM specialised for spatial understanding, including detecting object poses in 2D/3D, pointing, and even *predicting grasp poses*. Take a look at this demo. Details below. 🧵

Norman Di Palo

56,437 görüntüleme • 1 yıl önce

Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.
2:52

Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.

Robots Digest 🤖

14,663 görüntüleme • 1 ay önce

VLA-JEPA just dropped in LeRobot 🤖 What makes this model special is that it does not just learn what action to take from a given observation, it also leverages a JEPA world model to learn action-relevant dynamics. During training, the VLA leverages V-JEPA2 by conditioning its predictor. This clever trick adds a world modeling objective to the training, which also allows pretraining on human videos. At inference, the world model is dropped entirely, keeping only a standard VLA architecture: Qwen backbone and action head. The demo here was only fine-tuned on 13 examples, showing great pretraining capability and running in real time on NVIDIA Robotics DGX Spark! VLA-JEPA is the first world model to be ported to LeRobot, and I feel like it won't be the last 🚀 Thomas Wolf clem 🤗
0:35

VLA-JEPA just dropped in LeRobot 🤖 What makes this model special is that it does not just learn what action to take from a given observation, it also leverages a JEPA world model to learn action-relevant dynamics. During training, the VLA leverages V-JEPA2 by conditioning its predictor. This clever trick adds a world modeling objective to the training, which also allows pretraining on human videos. At inference, the world model is dropped entirely, keeping only a standard VLA architecture: Qwen backbone and action head. The demo here was only fine-tuned on 13 examples, showing great pretraining capability and running in real time on NVIDIA Robotics DGX Spark! VLA-JEPA is the first world model to be ported to LeRobot, and I feel like it won't be the last 🚀 Thomas Wolf clem 🤗

LeRobot

317,495 görüntüleme • 1 ay önce

NVIDIA CEO Jensen Huang just announced the launch of: Alpamayo “the world's first thinking, reasoning model for autonomous vehicles”
2:18

NVIDIA CEO Jensen Huang just announced the launch of: Alpamayo “the world's first thinking, reasoning model for autonomous vehicles”

Pirat_Nation 🔴

50,683 görüntüleme • 6 ay önce

Some great videos from the Gemini VLA release. These kinds of dexterous tasks require tons of coordination and interact with materials that are very hard to model in simulation. Good use of e2e learning and yet more evidence this is the right direction for robotics
0:33

Some great videos from the Gemini VLA release. These kinds of dexterous tasks require tons of coordination and interact with materials that are very hard to model in simulation. Good use of e2e learning and yet more evidence this is the right direction for robotics

Chris Paxton

30,949 görüntüleme • 1 yıl önce

New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact
43:58

New Episode: Carolina Parada leads the robotics team at Google DeepMind. Carolina believes in a future with a broad, rich ecosystem of diverse robot types, where AI is smart enough to embody any robot. We discuss her journey, advancements in Gemini Robotics 1.5, cross-embodiment transfer, RL versus imitation, humanoids, world models, scaling laws, societal concerns, and more. 1:17 Introduction 1:38 Career journey and inspirations 3:50 Reaction to the 2022 ChatGPT launch 4:53 Google DeepMind's robotics mission 9:13 Key upgrades in Gemini Robotics 1.5 14:06 Agentic system's web usage 15:33 Robotics data-gathering methods 16:57 VLA vs. reasoning model training differences 18:22 Convergence of action and reasoning models 19:35 Cross-embodiment transfer 22:34 Learning directly from humans 24:20 Generalization challenges 27:01 Imitation versus reinforcement learning 28:51 Use of world models in robotics 30:31 Applications and testing of Gemini Robotics 1.5 33:04 Do humanoids deserve special focus? 35:06 Home humanoids timeline & limiting factors 37:19 Scaling laws in robotics versus self-driving 38:46 Value of learning classical robotics approaches 40:38 Ethical issues and societal impact

The Humanoid Hub

71,481 görüntüleme • 8 ay önce

Gemini 2.5 is driving new advancements in robotics with its strong coding, spatial reasoning, and multimodal capabilities. 🦾
0:20

Gemini 2.5 is driving new advancements in robotics with its strong coding, spatial reasoning, and multimodal capabilities. 🦾

Google AI Developers

36,119 görüntüleme • 1 yıl önce

You have to watch this! For years now, I've been looking for signs of nontrivial zero-shot transfer across seen embodiments. When I saw the Alohas unhang tools from a wall used only on our Frankas I knew we had it! Gemini Robotics 1.5 is the first VLA to achieve such transfer!!
2:08

You have to watch this! For years now, I've been looking for signs of nontrivial zero-shot transfer across seen embodiments. When I saw the Alohas unhang tools from a wall used only on our Frankas I knew we had it! Gemini Robotics 1.5 is the first VLA to achieve such transfer!!

Konstantinos Bousmalis

119,825 görüntüleme • 9 ay önce

We took a robot to RSS in LA running our new Gemini Robotics On-Device VLA model. People interacted with the model with new objects and instructions in a brand new environment and the results were amazing!
0:48

We took a robot to RSS in LA running our new Gemini Robotics On-Device VLA model. People interacted with the model with new objects and instructions in a brand new environment and the results were amazing!

Ayzaan Wahid

19,669 görüntüleme • 1 yıl önce

I ran a quick test of Gemini Robotics ER 1.5 controlling my LeKiwi using the MCP I developed before. No extra code required - just specified a new model. Unfortunately, it doesn't work well, even though I had expected it to be the best, as it is the first major VLM model trained specifically for robotics applications. The video below is a relatively good attempt. It does understand what is going on and generates a reasonable action plan, but it is not doing well with the low-level control. I guess it was not trained for this and was used together with the VLA, so it is trained to generate more high-level commands like "grab this object and put it there" rather than doing low-level control specifying every robot movement step by step. Did anybody try to use it in a similar setup? What is your experience?
1:11

I ran a quick test of Gemini Robotics ER 1.5 controlling my LeKiwi using the MCP I developed before. No extra code required - just specified a new model. Unfortunately, it doesn't work well, even though I had expected it to be the best, as it is the first major VLM model trained specifically for robotics applications. The video below is a relatively good attempt. It does understand what is going on and generates a reasonable action plan, but it is not doing well with the low-level control. I guess it was not trained for this and was used together with the VLA, so it is trained to generate more high-level commands like "grab this object and put it there" rather than doing low-level control specifying every robot movement step by step. Did anybody try to use it in a similar setup? What is your experience?

Ilia

15,601 görüntüleme • 9 ay önce

MICROSOFT: "One of Grok 4’s headline features is its first-principles reasoning ability – essentially, the model tries to “think” like a scientist or detective, breaking problems down step by step."
0:34

MICROSOFT: "One of Grok 4’s headline features is its first-principles reasoning ability – essentially, the model tries to “think” like a scientist or detective, breaking problems down step by step."

DogeDesigner

19,246 görüntüleme • 9 ay önce

How to train a reasoning model, specifically Magistral from Mistral AI? Here is a quick video walkthrough of the paper.
2:10

How to train a reasoning model, specifically Magistral from Mistral AI? Here is a quick video walkthrough of the paper.

Sophia Yang, Ph.D.

31,752 görüntüleme • 1 yıl önce