🧵 New repo: Awesome Egocentric Dataset — a curated list of first-person vision datasets, refreshed beyond the classic Ego4D-era list. ✅ Recent 2023–2026 releases (Ego-Exo4D, EgoSchema, AEA, EgoExoLearn, EgoLife…) ✅ Dead/broken links removed, migrated ones fixed ✅ A section on egocentric-driven VLA papers (training source + data-processing), with first-principles + fishbone analysis 🚀 Open-source, contributions welcome. 🔗 #EgocentricVision# #FirstPersonVision# #VLA# #ComputerVision# #Datasets#

NEW ROBOT SKILL: Your humanoid can now play table tennis with you! It involves @TaouHuang, @junli_r84995, @kai_zhangjojo, @sephy_li, @ilnehc, and others. Called SMASH, it lets a Unitree G1 humanoid play table tennis from its own onboard cameras, with a single whole-body RL policy (no split upper/lower body). A head-mounted ZED X stereo camera tracks the incoming ball at 60 Hz. A downward-tilted ZED X Mini localizes the robot at 120 Hz by reading seven AprilTags placed on the table, and an Adaptive Extended Kalman Filter with a quadratic-drag ball-flight model fuses the two async streams into a joint estimate of ball state and robot pose at over 50 Hz. The policy is trained on a strike library of 400 real motion-capture demonstrations expanded by a motion VAE to about 5,000 clips, and produces coordinated full-body shots including smashes and low crouching returns. It replaces prior table-tennis humanoids that split upper and lower-body control and depended on external sensing. "No external cameras" does not mean no instrumentation: self-pose still depends on seven AprilTags on the table. The robot carries the cameras, but the downward ZED X Mini localizes it at 120 Hz by reading those printed fiducials (AprilTag plus PnP-RANSAC). The infrastructure moved from active (external cameras or mocap) to passive (printed tags) -> it did not disappear, so "onboard only" is softer than it first sounds. The honest return rate is 59.7 percent, not the headline 93.7. Over a 50-minute, 642-launch run, 93.7 percent is the contact rate, but 34.0 percent is contact-only, so about 40 percent of contacts do not come back over the net, successful returns are 59.7 percent. The backhand is the clear weak side: 38.9 percent return versus 66.7 forehand, with 52.1 percent of backhand contacts failing to return. The impressive number is contact; the useful number is return. In simulation it ties the HITTER baseline on success (86.38 vs 86.63). Its measurable edges are motion quality (whole-body MPJPE 75.0 vs 100.1) and the real-world capability HITTER lacks: consecutive rallies outdoors on onboard sensing alone. The follow-up beats the benchmark by moving to onboard egocentric vision, not by scoring higher on the shared metric. Using the head ZED X for pose instead of the downward ZED X Mini drops returns from 13/20 to 8/20; removing the adaptive observation-noise scaling drops to 7/20; and ablating the YOLO detector with a yellow distractor present takes detection to 0/20. The self-pose channel, not just ball tracking, is what makes returns land. One caveat: no measured smash, ball, or paddle speed appears anywhere, so "explosive whole-body smashes" is qualitative and the agility claim is unquantified in velocity terms. Besides, the project's claim "100-plus consecutive rallies" is marketing: the paper counts 642 ball launches with randomized landings, not 642 completed rallies.