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A new Science Robotics study highlights exoskeletons that allow physical therapists and patients recovering from stroke to “feel” each other’s movements as they walk on separate treadmills, enabling nuanced gait correction.

40,401 views • 1 month ago •via X (Twitter)

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Would you call a crab walk gait a functional neurological disorder? Not so fast. If you encounter someone walk with tiny, sideways shuffling steps, kinda like a crab, it could be a clue. In a striking case just published by Fraiman and colleagues, a crab walk-like helped to uncover a rare but important white matter brain disease called CSF1R-related leukoencephalopathy. Key Points: - A short-stepped laterally swaying crab-like gait may be an early sign of a higher-level gait disorder. - This disorder could originate from the brain’s motor planning centers, and not muscles or nerves. - These gait patterns may signal frontal lobe dysfunction, especially when strength, sensation and coordination remain intact. - When paired w/ behavioral symptoms and frontal white matter changes on MRI, this gait pattern may point to CSF1R-related leukoencephalopathy. - This syndrome is an underrecognized adult-onset leukodystrophy. My take: My mentor used to teach that a great neurologist always walked their patients to the examination room and in many cases he/she would clinch the diagnosis even before the door was shut and the vital signs collected. Here are 5 points that resonated w/ me about this case. 1- A strange walk might be a warning sign. Don't jump to a functional diagnosis if you see a sideways walk. 2- The brain can be the source of walking challenges even when the legs are strong. 3- White matter diseases can show up as a funny walk. Memory and speech challenges may emerge later. 4-Don’t ignore personality or speech changes. Disinhibition, apathy or trouble finding words acould point to a deeper brain condition. 5- A brain MRI and genetic test can unlock the mystery. #parkinson Parkinson's Foundation Norman Fixel Institute for Neurological Diseases

Michael Okun

11,129 views • 1 year ago

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 views • 11 days ago