Skip to content
May 12Tue
May 7Thu
Mar 18Wed
Mar 6Fri
  1. Toyota Research Institute Robotics77

    Toyota Research Institute explains statistical thinking for robot policy evaluation: from rigorous A/B testing to effective visualization

    The Toyota Research Institute (TRI) published a tutorial article introducing a statistical A/B testing method based on success/failure labels for robot policy evaluation.

    Why it matters: The TRI authors systematically explain statistical pitfalls and reusable tools in robot policy A/B testing, with runnable Python examples.

Jan 5Mon
  1. Pollen Robotics · YouTube45

    Reachy Mini LITE Assembly Guide

    Pollen Robotics has released an assembly guide video for the Reachy Mini LITE, demonstrating the full build process in three sections: foot, body, and head. In the body section, the 6 motors must be wired in sequence, with cables clipped into the base and routed through the head PCB; after checking rotation, the Tricap and head shell are secured. The head section covers installing the fisheye lens, glasses assembly, and antenna motor, then connecting the speaker, motors, power, and USB extension cable to complete the build.

Jan 2Fri
  1. Addverb Technologies18

    Is 2026 the Right Time to Automate Warehouses?

    Addverb Technologies proposes five core indicators—order volume and speed, return on investment, technological progress, competitive landscape, and seasonal fluctuations—to help companies judge the right timing for warehouse automation investment. The article notes that rising labor costs, surging e-commerce orders, and the need for 24/7 operations are pushing companies to adopt automation, and that AI can support real-time data analysis and predictive maintenance. It mentions that DHL's collaboration with Addverb is transforming warehouse operations by improving speed, accuracy, and scalability.

Oct 10Fri
  1. 天链机器人47

    Tianlian Robotics uses video learning to teach robots advanced motion

    Tianlian Robotics has published a video imitation learning training pipeline: extract reference motions with GVHMR/TRAM (or use motion capture datasets such as AMASS), then after human-to-robot joint mapping and training a motion orientation model, use PPO reinforcement learning in simulation with curriculum learning to progress from static poses to long-sequence complex motions, then optimize through domain randomization before deploying to the real robot, with fine-tuning via residual reinforcement learning or adaptive gains. The company says it has achieved key breakthroughs in dynamic force-control precision and stability, and that motions can also be reproduced in real robotic arm operation.