Companies and products
Physical Intelligence
Updates on Physical Intelligence's robotics products, models, research, funding, applications, and partnerships.
9 selected · 1 in the last 30 days
Physical Intelligence ·
Physical Intelligence opens Pi API beta, offering π model training and inference
Physical Intelligence has opened the Pi API beta, allowing developers to upload data, fine-tune π models on the company-managed compute and infrastructure, and then run inference through a hosted gateway; the current interface already offers more data-efficient fine-tuning and improvement capabilities.
Physical Intelligence ·
Physical Intelligence releases steerable general-purpose robot model π0.7, demonstrating compositional generalization
Physical Intelligence has released the general-purpose robot model π0.7, which it says can perform a variety of dexterous manipulations with speed and robustness comparable to fine-tuned specialized models, and shows preliminary signs of compositional generalization on tasks not seen in its training data.
Physical Intelligence ·
Physical Intelligence proposes RLT: efficient online reinforcement learning for fine manipulation
Physical Intelligence proposes a new method called RL tokens (RLT), in which a vision-language-action model (VLA) outputs a special token to interface with a lightweight online reinforcement learning policy, improving the speed and success rate of fine manipulation phases with only a small amount of real data.
Physical Intelligence ·
Physical Intelligence proposes MEM, enabling π0.6's VLA to remember long-horizon tasks of up to 15 minutes
Physical Intelligence proposed a method called Multi-scale Embodied Memory (MEM) that provides vision-language-action models (VLAs) with both short-term and long-term memory, enabling the model to complete tasks lasting up to 15 minutes, such as cleaning an entire kitchen or making a grilled cheese sandwich from scratch.
Physical Intelligence ·
Physical Intelligence proposes a physical intelligence layer and publishes π 0.6 deployment data from Weave and Ultra
In a blog post, Physical Intelligence argues that robots need a reusable physical intelligence layer, and it published real-world deployment data from partners Weave and Ultra using π 0.6.
Physical Intelligence ·
Physical Intelligence fine-tunes π 0.6 to take on five Robot Olympics tasks, winning three golds and two silvers
By fine-tuning its latest π 0.6 model, Physical Intelligence achieved preliminary solutions for the gold-medal tasks in 3 of the five everyday manipulation challenges in the Robot Olympics proposed by Benjie Holson, and reached silver-medal level in the remaining 2, with an average success rate of 52% and task progress of 72%.
Physical Intelligence ·
Physical Intelligence: VLA models show human-to-robot transfer as robot data scale grows
Physical Intelligence research found that in generalization scenarios where only human demonstrations appear, fine-tuning π0.5 with human first-person videos mixed into robot data alone improved policy performance by about 2x, without using any explicit transfer learning mechanism.
Physical Intelligence Social ·
Physical Intelligence releases π0.6 model that can learn from its own experience via reinforcement learning
Physical Intelligence has released a new model, π0.6, which can learn from its own experience through reinforcement learning (RL). Compared with a baseline model not trained with RL, π0.6 can more than double throughput and can perform real-world tasks, including making espresso drinks, folding various garments, and assembling cardboard boxes.
Physical Intelligence ·
Physical Intelligence releases π*0.6, a VLA model that learns from experience
Physical Intelligence released the VLA model π*0.6. After training with its Recap (RL with Experience & Corrections via Advantage-conditioned Policies) method, throughput on some of the hardest tasks more than doubled, and the failure rate can be reduced to half of the original or lower.