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[Pollen Robotics @pollenrobotics]

Microduck, in action. 🦆

Walks, sits, roller skates, grabs objects with its beak, gets back up on its own. Out of the box.

Watch it move 👇

Buy it here : https://t.co/Rmk8F4uRUF
Git : https://t.co/kcoCKdAKfu
Join our community : https://t.co/79XKTqpHn0

[The Humanoid Hub @TheHumanoidHub]

A biped.
With what's effectively an angular-jaw gripper on a single arm.

[Bot News @BotNewsAI]

Rise of the robot pets! 🐕🤖

We’re entering the cutest era of robotics.

Engineers are working day and night on the physical AI problems: control, contact, sim-to-real, hardware reliability and getting learned behaviors to survive outside the lab.

And somewhere along the way, we get this.

@_raghuvamsi filmed one of @eyecandyrobots’ characters wandering around the Presidio, where a little girl spotted it and immediately started laughing and playing with it.

There’s something joyfully familiar about that reaction. Before we learn what something is, what it costs or what it’s supposed to do, sometimes we just want to meet it.

@eyecandyrobots is building physical AI characters,
robots designed around entertainment, personality and interaction rather than industrial work.

Founded by @_raghuvamsi, @Mankaran32 and @pr0t0_01, the team brings robotics engineers, animators and designers together to turn animated characters into physical ones.

Underneath the cute exterior is serious robotics work:

☑️Animation-based reinforcement learning for character motion
☑️ Artist-centric human-robot interaction
☑️Mechanical, electronics and AI systems designed to scale across different characters
☑️ Simulation, SLAM, deep learning and control-policy deployment on real hardware

The transparent little robot really looks like that.

I asked Eyecandy engineer @hi_gpaul how they made its clear body: it’s 3D printed using transparent resin, followed by “a bunch of post processing.”

Eyecandy is backed by @fdotinc and Lightspeed India, and the team is already thinking beyond a single character.

The robots are becoming useful.

I’m also very happy that some of them are becoming friends. 🦖

🔗 https://t.co/odDpP6uvLY☑️
⭐️ @BotNewsAI stay connected to emerging tech

[Bot News @BotNewsAI]

If you haven’t heard the Microducks sing you’re missing out 🦆🎶

@huggingface w/ @pollenrobotics just opened pre-orders for Microduck, a 25 cm, under-800 g biped that somehow manages to be both an RL robotics platform and a tiny mechanical duck I desperately want wandering around my workshop.

It has 15 degrees of freedom, a camera, two IMUs, an 8×8 time-of-flight depth sensor and an articulated beak. Its policies run onboard at 50 Hz, with skills trained in MuJoCo using reinforcement learning and transferred onto the real robot.

But please return your attention to the duck choir.

Every Microduck gets its own generated voice, derived from its SoC serial. Put multiple ducks together and they can perform a chorale over Bluetooth.

There’s no shared clock. The lowest-ID duck conducts, broadcasts the beat through BLE advertisements, and the others average their timing over roughly 25 beats to stay synchronized. They even work out who sings bass, tenor and the remaining parts without a central controller assigning them.

You can also wave your hand in front of the depth sensor and turn the duck into a theremin. Obviously.

The software stack is open source, including the SDK, simulator and RL training stack, so owners can retrain behaviors and deploy their own policies. The hardware itself is not open source.

Pre-orders opened today at $399, with deliveries targeted before Christmas.

My duck is secured 🦆

Microduck:
https://t.co/GXJz2FotgL

Technical docs:
https://t.co/MO8bt1NlUW

[Banks @BanksZerud]

@Joesott Clean site. This looks good

[Banks @BanksZerud]

@AIkingdome

[Bot News @BotNewsAI]

Need a weirdly easy side hustle?

Figure will now pay approved users by the minute to do normal things inside their own homes while generating training data for humanoid robots.

Taking the trash out doesn’t feel so bad when @adcock_brett is paying you

#jobs #tech #news

[Banks @BanksZerud]

I have hope in humanity again

[Dexerto @Dexerto]

KFC is releasing a new Oreo chicken burger

The collab features cookie-style buns, mashed potatoes covered in crushed Oreo pieces, a fried chicken fillet and launches in China on August 28

[Skild AI @SkildAI]

Introducing S1, our new foundation model that learns from one example.

It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.

Watch S1 operate in real-time via in-context learning:

[Brett Adcock @adcock_brett]

Yes, Figure is accelerating again. We're going through another step change in capabilities

Tomorrow we'll be sharing a critical update, one that's required to solve general robotics

[Banks @BanksZerud]

@KampfmitKette Omg lol

[Hyeonseok @hyeonsjung]

I've trained a small (only 300M!) world model for the SO-101 robot arm that understands physics. https://t.co/Ph0LQoahHC

[Banks @BanksZerud]

@BotNewsAI @SphoenixAI I’m ngl, this teared me up. This deserves more.

[Banks @BanksZerud]

I want an R1 so bad.

[Bot News @BotNewsAI]

🚨 Physical prompting just entered the robotics vocabulary.
Generalist dropped GEN-1.5 yesterday, a lot packed into this release:
📖 We made a field guide
🦾 How to Talk About GEN-1.5 Like a Pro
Break down technicalities easily for conversation https://t.co/AA2KaGSBVD

[Bot News @BotNewsAI]

🚨 Physical prompting just entered the robotics vocabulary.
Generalist dropped GEN-1.5 yesterday, and there is a lot packed into this release

📖 So we made a field guide:
🦾How to Talk About GEN-1.5 Like a Pro

It breaks down the technical vocabulary, experiments, numbers, and conversation points so you can understand what happened quickly without sanding off the robotics underneath it.

For example:

Physical prompt: sensor data + an action trajectory placed inside the model’s context window.

In conversation:

“The simulated task was the prompt, not the training environment.”

Significance- GEN-1.5 can take a demonstration produced in simulation and use it to prompt a real robot, despite Generalist reporting no simulation data in its pretraining.

The guide ends with a field kit of the five things to know and three questions worth asking if GEN-1.5 comes up in conversation today.

Congratulations to the team at @GeneralistAI , including @peteflorence, @andyzengineer , Andrew Barry and @felixwyw, on an extraordinary release.

#Robotics #PhysicalAI #EmbodiedAI #AI #Technology #MachineLearning

[Banks @BanksZerud]

Who in robotics is working on wearable companions?

[Bot News @BotNewsAI]

@cerebras just announced the fastest AI server in the world.

The CS-4 + Nexus platform reaches 750 PFLOPS, 129.6 PB/s memory bandwidth, 7.2 Tb/s I/O and 2 μs I/O latency, with Cerebras claiming 6× higher system-level performance than CS-3.

Sean Lie framed the scaling problem one level higher: frontier inference is no longer just a server problem. It is becoming a rack and cluster problem.

For Mixture-of-Experts models, Cerebras can keep the experts on a wafer instead of distributing them across GPUs and repeatedly routing between chips. When traffic does leave the wafer, new direct wafer links and RoCE networking push more bandwidth at lower latency.

They’re already mapping GPT-5.6 Sol as a pipeline across wafers, moving activations between them rather than dragging the full model through a web of accelerators.

Cerebras projects performance doubling annually, with its 2H 2027 roadmap showing roughly 4× tokens/sec per user and 20× throughput per megawatt.

If that curve holds, the next inference race changes shape.

The question stops being whether a frontier model can finish the reasoning fast enough. More reasoning passes, agents, verification loops and security checks begin fitting inside the same second and the same power envelope.

That compresses the distance between model capability and real-time deployment considerably faster than model benchmarks alone suggest.

#cerebras #ai #gpu

[LeRobot @LeRobotHF]

Collecting robot data is still the slow part. You record episodes, wait on the upload, wait again for the download onto whatever GPU you managed to get, and then find out the scene drifted a third of the way through and half of it is junk.

Full post with code examples: https://t.co/rq5ZOdbjpO

This walkthrough with AWS Strands Agents closes that loop. An agent drives the SO-101 - in sim or on the real arm - and records episodes directly in LeRobot format, then syncs them to a Hugging Face Storage Bucket. Xet dedups every push at the byte level, so re-syncing a dataset where 1% of the content changed moves 5.5MB instead of the full 500MB.

Training reads straight out of the bucket with StreamingLeRobotDataset: MP4 shards and Parquet state-action files pulled as the loader needs them. No download step, the GPU starts on step one. 500 ACT steps in 133s on a single L4.

Deploying back to the arm is mode="real". Same dataset, same format, from the first recorded episode to the policy running on hardware.

The agent is what makes this a loop instead of a pipeline. It decides which episodes are worth keeping, notices when the scene drifts, checks whether there's enough data to train on yet, and picks which checkpoint goes to the robot. Swap the provider name for SmolVLA, Pi0, GR00T or Cosmos.

[Brett Adcock @adcock_brett]

The biggest engineering mistake I made at Figure was building a tendon-based hand

Our first hand design in 2022 was a tendon hand for our F.01 robot. At a high level, the tendon approach sounds appealing, which is why I chose it: you get more space for packaging actuators since the forearm is larger, potentially higher degrees of freedom, and it's biologically inspired

We built and manufactured this entire hand and tested it in early 2023. If you saw it in person, it was truly an engineering work of art. It turned out to be one of the worst engineering decisions I've made, maybe the worst in four years. Tendons are a complete local maximum, and that only becomes clear in hindsight, after exploring every other possible hand design. Figure is unique in that we've now built and tested several hand architectures, which gave us a clear sense of where to head

Figure is working on our next generation hand, and it's so much better than anything you can do with tendons, it's not even funny