Content types
Model releases
New robot models, new versions, or first-time weight releases. Benchmark results for an existing model belong under Benchmarks; changes only to pricing, quotas, or service availability belong under Product updates.
72 selected · 34 in the last 30 days
Sunday Robotics ·
Sunday Robotics previews ACT-2 model with 99.1% zero-shot success rate in clothes folding
Sunday Robotics previewed its robot model ACT-2, which achieved a 99.1% (±0.3%) zero-shot success rate in clothes folding across diverse unseen home environments with zero per-home adaptation, covering 9 clothing categories across 785 autonomous attempts, with an average folding quality of 4.72/5.
LimX Dynamics ·
LimX Dynamics releases humanoid VLA model V³-0 alongside COSA 0.5
LimX Dynamics released V³-0 alongside COSA 0.5, a humanoid VLA technology stack in its VLA-tools layer that supports mobile whole-body manipulation, running on the 43-DOF LimX humanoid robot Oli.
PsiBot/灵初智能 ·
Lingchu Intelligence and PKU-Lingchu Joint Lab Release and Open-Source Dual Dexterous Hand General Manipulation Model and Full-Stack System EgoSteer
Lingchu Intelligence and the PKU-Lingchu Joint Lab released and open-sourced the dual dexterous hand general manipulation model and full-stack system EgoSteer, comprising the data processing pipeline EgoSmith, the robotics technology stack Robot-Stack, and a world-model-enhanced VLA model.
蚂蚁灵波科技 · YouTube ·
LingBot-VA 2.0 released: a world-action model for physical-world robot control
Ant Group's Lingbo Technology has released LingBot-VA 2.0, which it describes as a world-action model pretrained from scratch for physical-world robot control, running real-time inference at 150 Hz on a single GPU and able to play air hockey, tidy desks, and pick chips from a conveyor belt.
蚂蚁灵波科技 · YouTube ·
Ant Lingbo Open-Sources Embodied Video Foundation Model LingBot-Video, Scoring 0.620 on RBench
Ant Lingbo has open-sourced LingBot-Video, a MoE video foundation model for embodied intelligence. It uses a DiT plus sparse MoE architecture with 30B parameters and 3B activated at inference, and is claimed to be about 3 times more efficient than dense models.
蚂蚁灵波科技 · YouTube ·
LingBot-VLA 2.0 open-sourced: supports 20 robot configurations and full-body control
LingBot-VLA 2.0 has been open-sourced. It is a next-generation embodied foundation model pretrained on 60,000 hours of real-world physical data, built on a redesigned data processing pipeline curated from 90,000 hours of robotic data and 20,000 hours of egocentric human videos.
智源研究院 · 微信公众号 ·
BAAI releases multimodal representation world model Wujie·RoboBrain Orca
BAAI has released the multimodal representation world model Wujie·RoboBrain Orca. Its core idea is to shift from predicting the next word, next frame, or next action to predicting the next world state: it first learns a unified latent world representation, then adapts to downstream tasks through three lightweight readout modules for text, images, and actions.
蚂蚁灵波科技 · YouTube ·
LingBot-Depth 2.0 spatial perception model for robots released, can see through glass and mirrors
LingBot-Depth 2.0 has been released, a next-generation spatial perception model for robots that focuses on perception of glass, mirrors, and transparent objects. It was trained on 150M-scale data (50 times that of v1.0) and took first place in 12 of 16 depth completion benchmarks; on the hardest indoor scenes, depth error RMSE dropped from 0.132 to 0.062.
X Square Robot/自变量机器人 ·
X Square Robot releases cross-modal embodied action tokenizer X-Tokenizer, with 13.5% gain in multimodal alignment and 8.25% gain in long-horizon tasks
X Square Robot has released X-Tokenizer, a cross-modal embodied action tokenizer built on a semantic residual quantization (SRQ) architecture and pretrained on 17 robotic arm series and 2.4 million trajectories (including 2 billion action frames). Compared with FAST, it improves multimodal alignment by 13.5% and long-horizon task performance by 8.25%, and scores 82.8 on RoboTwin 2.0; in real-robot tests it outperforms comparison methods such as FAST and 4-level RVQ.
HUMANOID ·
KinetIQ Ascend uses real-world reinforcement learning to boost humanoid robot manipulation throughput and success rate
HUMANOID has launched KinetIQ Ascend, an end-to-end visual reinforcement learning method that operates under production deployment conditions, extending its KinetIQ framework to let robots improve manipulation skills through trial and error rather than relying solely on human demonstration; the company says this is the first such method publicly demonstrated on production-line humanoid robot manipulation.
OFweek Robotics ·
Galbot releases AstraBrain-WBC 0.5, validating Scaling Law with humanoid motion data
Galbot released AstraBrain-WBC 0.5 (paper titled Humanoid-GPT, accepted to CVPR 2026), training a Transformer whole-body controller on about 2 billion frames of retargeted motion data, achieving a 92.58% zero-shot motion tracking success rate with 0.39 ms inference latency; code and data are open-sourced.
Figure AI ·
Figure AI shows two Helix-02 humanoid robots collaborating to tidy a bedroom
Figure AI demonstrated two humanoid robots equipped with Helix-02 collaborating to tidy a bedroom within two minutes, completing tasks such as opening a door, hanging clothes, putting away headphones, closing a book, taking out trash, pushing a chair under a desk, and making a bed together. The two robots run the same learned vision-language-action (VLA) policy, with no shared planner, message passing, or central coordinator; each infers the other's intent from its actions via its own cameras. The company says that to its knowledge this is the first demonstration of multi-humanoid collaborative mobile manipulation completed by a single learned neural network directly from pixels to actions.
PsiBot/灵初智能 ·
Lingchu Intelligence releases Psi-R2 and Psi-W0, open-sources 1,000-hour full-modality human hand manipulation dataset
On April 10, Lingchu Intelligence released Psi-R2, pretrained on human data, and the Action-Conditioned World Model Psi-W0, and open-sourced the first 1,000 hours of a full-modality human hand manipulation dataset, with total data reserves reaching 100,000 hours.
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.
PsiBot/灵初智能 ·
Lingchu Intelligence Releases Psi-R2 and Psi-W0 Dual Models, Pretrained on 100,000 Hours of Human Data
Lingchu Intelligence released two models, Psi-R2 and Psi-W0, which it calls the industry's first embodied intelligence models pretrained on human data at the 100,000-hour scale.
Figure AI ·
Figure AI Releases Helix 02: A Full-Body Autonomous Control Model for Humanoid Robots
Figure AI has released Helix 02, extending control from the upper body to the entire humanoid robot and enabling continuous, integrated autonomous tasks combining locomotion and manipulation. It adds a new System 0 whole-body controller, trained on over 1,000 hours of human motion data and simulation-based reinforcement learning, which outputs joint commands at 1 kHz via a 10M-parameter network, replacing roughly 109,504 lines of hand-written C++ code; System 1 connects head-mounted vision, palm cameras, fingertip touch sensing, and proprioception, outputting whole-body joint targets at 200 Hz.
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.
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.