Gemini Robotics 2 brings whole body intelligence to robots
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
More from the blog
Welcome Beam: a 501B open model you can own, customize, and scale
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Proof-based general-purpose computing with Magnetar
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Welcome FLUX.3 Image Klein in Diffusers
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Related papers
RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
RMBench is introduced, a simulation benchmark comprising 9 manipulation tasks that span multiple levels of memory complexity, enabling systematic evaluation of policy memory capabilities and Mem-0, a modular manipulation policy with explicit memory components designed to support controlled ablation studies.
StarVLA-α: Reducing Complexity in Vision-Language-Action Systems
This work introduces StarVLA-$\alpha, a simple yet strong baseline designed to study VLA design choices under controlled conditions that deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis.
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
This work characterize models from this family of methods as JEPA-WMs and investigates the technical choices that make algorithms from this class work, and proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.
Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for Long-Horizon Embodied Navigation
A novel vision-language model (VLM)-based navigation framework that addresses challenges by adaptively retrieving task-relevant cues from a global memory module and integrating them with the agent's egocentric observations and enhances spatial reasoning and decision-making in long-horizon tasks.