physical-intelligence-8371f368·3 events·first seen Aliases: Physical Intelligence
Berkeley Artificial Intelligence Research (BAIR) Lab published its 2026 graduate showcase, highlighting PhD completions across LLMs, robotics, AI safety, computer vision, and human-AI interaction. Notable placements include a graduate joining OpenAI as Member of Technical Staff (LLM reasoning), one joining Physical Intelligence (generalist vision/robotics), one joining Mistral AI as AI Scientist, and one becoming an Assistant Professor at UCLA. The cohort's research themes span test-time vs. pretraining scaling tradeoffs, LLM fairness and calibration, dexterous manipulation, and generative modeling for proteins.
Hugging Face published a blog post covering π0 and π0-FAST, vision-language-action (VLA) models developed for general-purpose robot control. These models combine vision and language understanding with action generation to enable robots to perform a broad range of manipulation tasks. The post appears to be a technical overview or release commentary on Physical Intelligence's robotics foundation models, situating them within the broader VLA research landscape.
DexHoldem is a new system-level benchmark for evaluating dexterous embodied agents on a ShadowHand robot performing Texas Hold'em card manipulation tasks. It provides 1,470 teleoperated demonstrations across 14 manipulation primitives, a physical policy benchmark, and an agentic perception benchmark for structured game-state recovery. Top performers include π₀.₅ at 61.2% task completion and Claude Opus 4.7 at 34.3% strict perception accuracy, with GPT 5.5 achieving 66.8% field-wise accuracy. The benchmark exposes gaps between isolated visual sub-capabilities and full closed-loop embodied decision-making.