Install
Please confirm you are human
This browser or connection looks automated. Press and continuously hold the control for 3 seconds to enable Google-hosted web results and, when separately allowed, AI-assisted answers.
A successful check enables 100 search requests. Interactive access does not authorize scraping, systematic collection, or reuse of search output.
News
Vention Opens Physical AI Lab to Bridge AI Research and Scalable Industrial Deployment
3+ day, 23+ hour ago (57+ words) Vention, the leading digital-first industrial automation platform, announced the opening of its Physical AI lab in Montreal. The lab is led by Dr. Jimmy Li, Vention's Director of Physical AI, and advised by Dr. Joelle Pineau, Chief AI Officer at…...
Vention launches physical AI lab to make robots better at manufacturing
4+ day, 19+ hour ago (603+ words) The Montreal-based firm has 16 researchers on staff and has recruited star compute scientist Joelle Pineau as an advisor to the unit Vention, a Montreal-headquartered robotics firm, has set up a research lab focused on physical AI that will use the…...
Cross-domain remote sensing scene classification via Swin Transformer with domain-invariant feature learning
1+ week, 3+ day ago (15+ words) Springer Nature Link...
Building and Testing Robot Policies with MuJoCo
6+ day, 8+ hour ago (267+ words) MuJoCo has become one of the go-to physics simulators for robot learning — it's fast, accurate for contact-rich manipulation, and has first-class Python bindings via mujoco and dm_control. This tutorial walks through setting up a robot environment in MuJoCo, wiring it up…...
Sim-to-Real Transfer for Physical AI Robots
6+ day, 8+ hour ago (291+ words) A policy that hits 95% success in simulation and 20% on the real robot is one of the most common — and most frustrating — outcomes in robot learning. The gap between simulated and real-world dynamics, sensing, and visuals is called the sim-to-real gap,…...
Domain Randomization for Robust Robot Learning
6+ day, 8+ hour ago (191+ words) Domain randomization is the idea that, instead of trying to perfectly match simulation to reality, you randomize simulation parameters widely enough that reality just looks like "one more variation" the policy has already seen. It's one of the highest-leverage techniques…...
Building an Imitation Learning Pipeline for Robotic Manipulation
6+ day, 8+ hour ago (259+ words) You've collected demonstrations — now it's time to turn them into a working policy. This tutorial walks through building an end-to-end imitation learning pipeline: dataset loading, model architecture, training loop, and evaluation, using the demonstration format from the previous tutorial. Wrap…...
Robot Policy Evaluation: Why 90% vs 92% Proves Little
1+ week, 1+ day ago (591+ words) Statements such as “Policy A achieves 90 % success; Policy B achieves 92 % success” are ubiquitous in robotics papers and technical reports. However, these two numbers alone cannot support the conclusion that Policy B is stronger. Valid interpretation must account for roll‑out…...
Agile Robots Brings Physical AI into the Real World - From Industrial Automation to Robot Training Data
1+ week, 5+ day ago (362+ words) From intelligent robotic automation in Zurich to bimanual robot training data in Bremen, Agile Robots and its subsidiary Franka Robotics demonstrated how Physical AI empowers human from research into real-world applications. ZURICH and BREMEN, Germany, Sept. 2, 2026 /PRNewswire/ -- This August, at…...
Building Synthetic Training Data Pipelines for Robotics
1+ week, 4+ day ago (223+ words) Synthetic data pipelines are most valuable for: It's not a full replacement for real data — visual and dynamics gaps mean synthetic-only training rarely matches real-data performance for the hardest tasks — but as a large, cheap, well-labeled complement to a smaller…...