Diffusion-based Vision-Language-Action policies achieve remarkable success in robotic manipulation, yet commit a fundamental geometric error we term the $\textbf{Euclidean Fallacy}$: representing SE(3) poses as flat $\mathbb{R}^{12}$ vectors. This approximation induces (1) manifold drift violating SO(3) constraints, (2) broken equivariance under coordinate transformations, and (3) non-geodesic trajectories with excessive kinematic cost.
grasp-results-table table { font-size: 0. 875rem; line-height: 1.
Gemini Robotics ER 1. 6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics.
Are there any genies that can be put back in the bottle?
By Jack Clark
Google DeepMind researches AI's harmful manipulation risks across areas like finance and health, leading to new safety measures.
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects.
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer . Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges ), and scales well to long-horizon tasks.
We’re powering an era of physical agents — enabling robots to perceive, plan, think, use tools and act to better solve complex, multi-step tasks.
We’re introducing an efficient, on-device robotics model with general-purpose dexterity and fast task adaptation.