Technology areas
Robot models and learning
Robot VLA models, general action and world models, imitation and reinforcement learning, skill and motion policy learning, online adaptation, and transfer across tasks and robot bodies. The contribution is a model, policy, or learning method. Dataset releases belong under Data and simulation; inference deployment belongs under Software and compute systems.
206 selected · 116 in the last 30 days
Skild AI ·
Skild AI announces completion of $1.4 billion Series C funding, valuation exceeds $14 billion
Skild AI announced the completion of a $1.4 billion Series C funding round led by SoftBank, with participation from NVentures (NVIDIA), Macquarie Capital, Jeff Bezos, and others, at a valuation exceeding $14 billion.
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.
Pieter Abbeel ·
Amazon FAR open-sources Holosoma, a full-stack sim-to-real codebase for humanoid robots
Amazon FAR has released Holosoma, a full-stack solution, and open-sourced a complete codebase covering multiple simulation backends, training, motion retargeting, and real-world inference. Pieter Abbeel shared and added that the infrastructure was built for humanoid robots and can also be readily adapted into a quadruped version (the quadruped part is already included), and that it contains a great deal of infrastructure experience and convenience tools the team relies on daily.
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.
LimX Dynamics ·
GVF-TAPE Algorithm from LimX Dynamics and Partners Accepted to CoRL 2025, Enabling Robots to Learn Manipulation from Video
The paper "Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-top Manipulation," a collaboration between SUSTech CLEAR Lab, LimX Dynamics, and the University of Hong Kong, has been accepted to CoRL 2025.
Figure AI ·
Figure launches Project Go-Big: Helix learns navigation zero-shot from human first-person video alone
Figure announced two advances in Project Go-Big: a partnership with Brookfield to build a large-scale humanoid pretraining dataset, with Brookfield holding over 100,000 homes, 500 million square feet of commercial office space, and 160 million square feet of logistics space; and Helix, after training on 100% first-person human video, responding to natural-language commands such as "go to the fridge" without any robot demonstrations, navigating cluttered real home environments.
Figure AI ·
Figure AI's Helix model achieves fully autonomous towel folding on a humanoid robot
Figure AI's VLA model Helix fully autonomously folds towels, which the company says is the first time a multi-fingered humanoid robot has completed this task using an end-to-end neural network. With the architecture and training hyperparameters unchanged and only a new dataset added, it can pick items from a mixed towel pile, adjust its folding strategy based on the initial shape, correct multiple grasping errors, and untangle twisted towels using fine motions such as tracing edges with its thumb and pinching corners.
Figure AI ·
Figure AI reports three-month Helix logistics deployment progress: 4.05 seconds per item, about 95% barcode success rate
Figure AI released progress on Helix's capabilities after three months of deployment in a logistics environment: per-item processing time fell from about 5.0 seconds to 4.05 seconds, barcode scanning success rate rose from about 70% to about 95%, and the system gained the ability to handle flexible packages such as poly bags and flat envelopes.
Figure AI ·
Figure AI uses reinforcement learning to give Figure 02 humanoid robot a natural, human-like walk
Figure AI released an end-to-end reinforcement learning walking controller that lets the Figure 02 humanoid robot learn a human-like gait in simulation by imitating human reference trajectories through rewards, including heel strike, toe push-off, and arm swing. Training runs in parallel across thousands of robots in GPU-accelerated physics simulation, collecting years of simulated data in hours, and combines domain randomization with kHz-level torque feedback control to achieve zero-shot transfer to the real robot; 10 Figure 02 units run the same neural network without individual tuning.