Reinforcement Learning for Active Perception in Autonomous Navigation. [📍GitHub & Paper ] Most robots navigate as if their cameras were nailed in place. But perception is not passive. Animals move their heads and eyes constantly to decide where to go next. Robots should do the same. That is the idea behind “Reinforcement Learning for Active Perception in Autonomous Navigation,” which has just been accepted at ICRA 2026. Instead of treating navigation and perception as two separate problems, this work trains flying robots to do both at once. The robot does not only decide where to move. It also decides where to look. Using reinforcement learning, the robot learns to: •fly safely through cluttered environments, •actively reorient its onboard camera to reduce uncertainty, •balance reaching a goal with gathering better visual information. The key result is that actively controlling perception makes navigation safer. In simulation and on a real flying robot, this approach consistently outperforms static-camera baselines. The sim-to-real transfer holds up, which is usually where things break. This is a small but important shift in how we think about autonomy. Better planning does not always come from better maps or bigger models. Sometimes it comes from simply looking in the right direction at the right time. Thanks for sharing, Kostas Alexis! 📍Code: Paper: Video: —— Weekly robotics and AI insights. Subscribe free:

Reinforcement Learning for Active Perception in Autonomous Navigation. [📍GitHub & Paper ] Most robots navigate as if their cameras were nailed in place. But perception is not passive. Animals move their heads and eyes constantly to decide where to go next. Robots should do the same. That is the idea behind “Reinforcement Learning for Active Perception in Autonomous Navigation,” which has just been accepted at ICRA 2026. Instead of treating navigation and perception as two separate problems, this work trains flying robots to do both at once. The robot does not only decide where to move. It also decides where to look. Using reinforcement learning, the robot learns to: •fly safely through cluttered environments, •actively reorient its onboard camera to reduce uncertainty, •balance reaching a goal with gathering better visual information. The key result is that actively controlling perception makes navigation safer. In simulation and on a real flying robot, this approach consistently outperforms static-camera baselines. The sim-to-real transfer holds up, which is usually where things break. This is a small but important shift in how we think about autonomy. Better planning does not always come from better maps or bigger models. Sometimes it comes from simply looking in the right direction at the right time. Thanks for sharing, Kostas Alexis! 📍Code: Paper: Video: —— Weekly robotics and AI insights. Subscribe free:

This seems cool: up to 4.5kg grasp load (though very much depending on grasp), seems like it has passive/mechanical backdrivable joints, and active stiffness control. Seems much, much more like a human hand than most things I have seen. Implemented in part via a low-backlash, low-damping reducer; which is interesting. Potentially how they avoid the need for qdd/low gear ratio actuators, but still get mechanical transparency.

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

A student built a real anti-gravity machine… using an Arduino. How to Make an Acoustic Levitator: Arduino Nano + motor driver + about 60 ultrasonic transducers. They all emit ~40 kHz sound. The sound waves meet and form fixed pockets in the air. Tiny bits of styrofoam get stuck in those pockets and just hang there. If you put your hand in, the pattern breaks and they fall. Same principle labs use to move droplets or samples without touching them. Credit: u/williamlk5341 on r/arduino Based on an “Acoustic Levitator” Instructable guide: ---- Weekly robotics and AI insights. Subscribe free:

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to @SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

A full MIT course on visual autonomous navigation. If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️ MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms. What it focuses on: • 2D and 3D vision for navigation • Visual and visual-inertial odometry for state estimation • Place recognition and SLAM for localization and mapping • Trajectory optimization for motion planning • Learning-based perception in geometric settings All material is available publicly, including slides and notes. 📍 If you know other solid resources on vision-based autonomy, feel free to share them. —- Weekly robotics and AI insights. Subscribe free:

Tiny actuators. Accurate down to about 25 nanometers. Xeryon builds piezo actuators designed for positioning tasks where normal motors simply are not precise enough. • Repeatability around ±25 nm • Linear, rotary, and multi-DoF configurations • Used in metrology, semiconductor tooling, and laser systems At this scale, motion is no longer about speed or power. It is about staying exactly where you are supposed to be. —— Weekly robotics and AI insights. Subscribe free:

A full MIT course on visual autonomous navigation. If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️ MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms. What it focuses on: • 2D and 3D vision for navigation • Visual and visual-inertial odometry for state estimation • Place recognition and SLAM for localization and mapping • Trajectory optimization for motion planning • Learning-based perception in geometric settings All material is available publicly, including slides and notes. 📍 If you know other solid resources on vision-based autonomy, feel free to share them. —- Weekly robotics and AI insights. Subscribe free:

A Bimanual Robot for Dynamic Manipulation AthenaZero just made the September cover of Science Robotics. {📌 Worth saving. I linked the full RAI Institute blog further down in the post if you want to dive deeper into AthenaZero’s design and control approach.} Researchers at the @rai_inst built a low-inertia bimanual robot to study one of the hardest problems in robotics: dynamic manipulation. And the first demos are pretty wild. AthenaZero can throw, catch and bat a baseball at speeds approaching human performance. • Throwing: up to 113 km/h • Catching: up to 66 km/h • Batting: around 50 km/h • Catching and batting over just 7.3 meters That short distance puts serious pressure on reaction times. But the interesting part isn't baseball. Most robot arms use high gear ratios that make them strong and precise, but also stiff and difficult to move compliantly during contact. AthenaZero takes a very different approach. Its arms use low gear ratios, as low as 5:1 in most joints, and have an effective mass at the wrist of only 3.97 kg. That's much closer to a human arm than a conventional collaborative robot. The result is a robot that can accelerate quickly, react to changing trajectories and absorb contact instead of simply fighting against it. The researchers demonstrated robot-to-robot and human-to-robot catching and batting, with the robot continuously adapting its motion to the incoming ball. Dynamic manipulation remains one of the hardest open problems in robotics. But systems like AthenaZero show what becomes possible when robots are designed for fast, physical interaction from the beginning. Blog: —— Weekly robotics and AI insights. Subscribe free:

Mini 6 dof Arm. 3D printed planetary gearboxs & more… [📍GitHub link below ] A mini 6-axis arm driven by stepper motors with custom 3D printed split ring planetary gearboxs and an inverted belt differential wrist with custom bearings, driven by low-cost stepper motors and TMC5150 drivers. Custom firmware was written in C for the STM32 MCU on an BTT Octopus board, to allow for full closed loop PID control using AS5048a encoders daisy-chained over SPI. The controller takes joint targets and returns joint states to a Raspberry Pi 5 streamed over CAN bus. All credit to @JamesGullberg: 📍GitHub: —— Weekly robotics and AI insights. Subscribe free:

BREAKING: @watneyrobotics just raised an $80M Series A and says it already runs the largest 24/7 fleet of dexterous robots inside hyperscaler data centers. Congrats @rgannon_ and @Grant__Gordon! 💃

Six people. 600 square feet. A dual-arm robot they're teaching to help build itself. San Francisco, California Inside Almond Robotics: Building America’s Robot Factory I sat down with @saba_khalilnaji, Co-Founder & CEO of @almond_robotics, to talk about making factory automation accessible to more businesses. Our conversation started on the factory floor. Then drilling was about to begin, so Saba took us into the stairwell, with a quick look at the mobile version of Axol along the way. Before Almond, Saba spent six and a half years at DoorDash, watching the company grow and learning what it takes to deliver reliably at scale. Now he's applying those lessons to manufacturing. The problem: automating a factory task can cost six figures, with extensive custom engineering. Change the product or process, and that solution may need substantial rework. Almond builds Axol for a new generation of integrators using AI and human demonstrations to teach robots new tasks. The ambition: give those teams the tools to get a deployment running in a week. We cover: › What DoorDash taught Saba about speed, ownership & scaling › What visiting factories in Chicago revealed about demand for automation › The rise of the “neo-integrator” › Making robot training data easier to collect › Teaching Axol to help manufacture its own parts › What it takes to build robots at scale Saba on what factories actually look like: “Factories today are just like rooms. With people and machines, and people are sort of like the glue between the machines.” That is the opportunity: helping factories automate the work between machines, even when the parts and orders keep changing. Inside Almond: › Six-person team building robots in San Francisco › Axol already shipping to customers › Mantis handheld grippers for collecting robot training data › AI agents supporting internal inventory and supply chain work › Axol sanding metal parts used in the robot itself › Next milestone: hundreds of Axol robots deployed Full conversation with Saba below. This podcast is sponsored by @CoreWeave . 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Inside Almond’s factory (01:02) A sneak peek at the mobile Axol (02:51) Soldering at nine & building robots in the garage (09:33) Winning a robotics competition, then failing at autonomy (11:46) Why Saba chose bioengineering at Berkeley (14:58) Joining DoorDash & staying for six and a half years (18:18) How bureaucracy slows a startup down (20:16) Making decisions & living with the consequences (22:20) Leaving a stable job to build a company (29:40) What factory visits revealed about automation (33:43) Validating demand before building hardware (34:48) Axol & the rise of the neo-integrator (38:35) Why deployments are the north star (40:16) Building America’s robot factory (42:20) The supply chain lessons that come with shipping (43:08) Teaching a robot to help build itself (45:10) How U.S. sourcing shapes the robot (46:09) Advice to his younger self (47:38) Why “Almond”?

Are you kidding me?? .@adcock_brett?? I need to give it to @Rewkang, especially @intern, @GoingBallistic5, @JacklouisP, and the rest of the @RoboStrategy team... They are pushing super hard to become THE room for robotics. Excited for this!

Six people. 600 square feet. A dual-arm robot they're teaching to help build itself. San Francisco, California Inside Almond Robotics: Building America’s Robot Factory I sat down with @saba_khalilnaji, Co-Founder & CEO of @almond_robotics, to talk about making factory automation accessible to more businesses. Our conversation started on the factory floor. Then drilling was about to begin, so Saba took us into the stairwell, with a quick look at the mobile version of Axol along the way. Before Almond, Saba spent six and a half years at DoorDash, watching the company grow and learning what it takes to deliver reliably at scale. Now he's applying those lessons to manufacturing. The problem: automating a factory task can cost six figures, with extensive custom engineering. Change the product or process, and that solution may need substantial rework. Almond builds Axol for a new generation of integrators using AI and human demonstrations to teach robots new tasks. The ambition: give those teams the tools to get a deployment running in a week. We cover: › What DoorDash taught Saba about speed, ownership & scaling › What visiting factories in Chicago revealed about demand for automation › The rise of the “neo-integrator” › Making robot training data easier to collect › Teaching Axol to help manufacture its own parts › What it takes to build robots at scale Saba on what factories actually look like: “Factories today are just like rooms. With people and machines, and people are sort of like the glue between the machines.” That is the opportunity: helping factories automate the work between machines, even when the parts and orders keep changing. Inside Almond: › Six-person team building robots in San Francisco › Axol already shipping to customers › Mantis handheld grippers for collecting robot training data › AI agents supporting internal inventory and supply chain work › Axol sanding metal parts used in the robot itself › Next milestone: hundreds of Axol robots deployed Full conversation with Saba below. This podcast is sponsored by @CoreWeave . 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Inside Almond’s factory (01:02) A sneak peek at the mobile Axol (02:51) Soldering at nine & building robots in the garage (09:33) Winning a robotics competition, then failing at autonomy (11:46) Why Saba chose bioengineering at Berkeley (14:58) Joining DoorDash & staying for six and a half years (18:18) How bureaucracy slows a startup down (20:16) Making decisions & living with the consequences (22:20) Leaving a stable job to build a company (29:40) What factory visits revealed about automation (33:43) Validating demand before building hardware (34:48) Axol & the rise of the neo-integrator (38:35) Why deployments are the north star (40:16) Building America’s robot factory (42:20) The supply chain lessons that come with shipping (43:08) Teaching a robot to help build itself (45:10) How U.S. sourcing shapes the robot (46:09) Advice to his younger self (47:38) Why “Almond”?

BREAKING: @watneyrobotics just raised an $80M Series A and says it already runs the largest 24/7 fleet of dexterous robots inside hyperscaler data centers. Congrats @rgannon_ and @Grant__Gordon! 💃

Hot take: putting real precision gearboxes and actuators into series production is the bottleneck. China is pumping out volume, yes. From what I hear, the high-precision things are mainly still not there. So now the question... do you think that’s where Europe’s hidden champions come in? Wrong answers only. (Video is mine. After all the Astra hype the last few days I just wanted to play around with it too. Not affiliated with Schaeffler or Bosch.)