Technology areas
Data and simulation
Demonstration, teleoperation for data collection, motion retargeting, robot datasets, data quality, physics simulation, synthetic data, digital twins, domain randomization, and sim-to-real transfer. The focus is data, environments, and tools for training and validation. Simulation used only to validate a new learning algorithm does not automatically qualify.
136 selected · 91 in the last 30 days
新智元 · 微信公众号 ·
AgiBot releases GO-3 Preview embodied model, tops three robotics leaderboards and plans full open source by year-end
AgiBot officially released its GO-3 Preview embodied model on October 29, taking Top 1 on robotics leaderboards including RoboDojo, RoboTwin, and RoboColiseum, and expects to fully open source it by year-end.
戴盟机器人 ·
Daimon Robotics completes hundreds of millions in strategic financing, led by Ant Group
Daimon Robotics announced the completion of a strategic funding round of hundreds of millions, led by Ant Group with existing shareholders oversubscribing, just two months after its hundred-million-yuan Series A. The company also launched its tactile-anchored world model Daimon-TWM, with parameter scale at the 10B level, capable of real-time inference on the NVIDIA RTX 5090 platform, and claims that its average success rate on contact-intensive manipulation tasks is twice that of π0.5. Daimon has also built the tactile full-modality dataset Daimon-Infinity; after the first 10,000 hours of data were open-sourced, it received nearly 5 million downloads on the ModelScope community.
云启资本 ·
PHYMI Tech releases first robot Sagi, featuring four-wheeled-legged humanoid configuration and PHYMI BRAIN 1.0
PHYMI officially released its first robot, Sagi, on October 28. It adopts a "four-wheeled-legged humanoid configuration" combining four wheeled legs with a humanoid upper body, dual arms, and a rear box, targeting scenarios such as office delivery, community errands, and outdoor camping transport.
HuggingFace Daily Papers ·
CaPtain: A Post-Training Pipeline for Code-as-Policy Robot Agents
CaPtain proposes an open post-training pipeline that trains small open vision-language models such as Qwen3.5-4B into code-as-policy (CaP) robot agents that do not rely on VLAs, frontier models, or privileged state at test time.
AgiBot ·
AgiBot releases embodied foundation model GO-3 Preview, ranks first on three embodied intelligence benchmarks
AgiBot has released the embodied foundation model GO-3 Preview, built on its self-developed WRAM (World Reasoning Action Model) architecture that unifies task understanding, future state prediction, and action generation, and it ranks first on three embodied intelligence benchmarks: RoboDojo, RoboTwin, and RoboColiseum. It is planned to be fully open-sourced by the end of 2026.
36氪 · 微信公众号 ·
Google DeepMind releases world model Genie 4, supporting real-time interaction and native audio
Google DeepMind released its next-generation general world model Genie 4 on the evening of the 28th. It can generate and modify interactive scenes in real time based on prompts, sustaining a single coherent world for about 2 minutes at roughly 24 frames per second.
简智机器人 · X ·
GenRobot.AI's two open-source datasets enter Hugging Face robotics dataset trending top five
GenRobot.AI announced that both of its open-source datasets, Gen-HumanWholeBody and Gen-HumanEgo, have entered the top five of the Hugging Face robotics dataset trending list. Gen-HumanWholeBody is a first-person whole-body manipulation dataset containing 600 hours of whole-body manipulation data, 16,976 real-world scenes, and 1,281 human skills, with whole-body MPJPE of about 3 cm; the authors say it targets whole-body motion learning, human-to-robot transfer, and embodied foundation model research.
HuggingFace Daily Papers ·
RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions
The research team proposes RynnWorld-Latent, a cross-embodiment video world model that uses the 3D-grounded latent actions of RynnLAM as a shared conditioning interface, enabling joint pretraining on human interaction videos, robot demonstrations, and simulation data.
arXiv cs.RO ·
FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors
The research team proposes FTP-1, a generalist foundation tactile policy pretrained across multiple tactile sensors and embodiments. It uses heterogeneous encoders to map image-, array-, and state-based tactile signals into unified morphology-aware latent tokens, which are jointly modeled by a shared tactile Transformer expert.
arXiv cs.RO ·
Rendering-Free Event Frame Simulation Accelerates Reinforcement Learning for Autonomous Robot Navigation
The authors propose an analytic event simulator that synthesizes event streams directly from scene geometry and ego-motion without image rendering, generating 64x64 event observations in just 17 microseconds on a single GPU, over 400 times faster than state-of-the-art rendering pipelines. Reinforcement learning training for an event-camera obstacle-avoidance policy based on this simulator converges in under 30 minutes, three orders of magnitude faster than training on rendered events, and transfers zero-shot to ray-traced event simulation in Isaac Sim.
Shanghai AI Laboratory - Embodied Intelligence ·
Shanghai AI Laboratory and partners jointly launch materials multimodal large model "Caihua·MatVerse"
Suzhou National Laboratory, Shanghai AI Laboratory, Shanghai Jiao Tong University and others jointly launched the "Caihua·MatVerse" materials multimodal large model and agent application system, which uses an "independent encoders plus unified decoder" architecture to align XRD spectra, electron microscope images and molecular structures into the same semantic space.
Union Square Ventures · X ·
USV and Creandum co-lead $40 million seed round for Machenta
USV announced that it co-led Machenta's $40 million seed round with Creandum, as the company also emerged from stealth. Machenta says its robotics foundation model is trained entirely in simulation, with no real-world data, and can learn a task from a single demonstration without additional training, adapting when the environment changes.
机器之心 · 微信公众号 ·
Ropedia Releases Six Research Results, Exploring How First-Person Human Data Can Be Turned into Robot Capabilities
Ropedia released six studies at the first Ropedia Super Week, held October 19 to 23, centered on how first-person human video can be turned into training and execution information usable by robots.
Kinetix AI · YouTube ·
Kinetix AI releases KAI World Model, supporting locomotion and manipulation tasks
Kinetix AI has released the KAI World Model, which is based on parallel video generation and 4D spatial modeling and can simultaneously generate virtual worlds and reconstruct spatial scenes. The model was pre-trained on millions of videos and supports pure locomotion, pure manipulation, and coordinated locomotion-manipulation tasks; it claims generalization across tasks and across robot embodiments. Live demonstrations include a robotic arm autonomously opening a beverage can and a humanoid robot playing real-time table tennis rallies.
arXiv cs.RO ·
X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control
X-WBC is a cross-embodiment humanoid whole-body control foundation framework that separates relatively shared human motion semantics from embodiment-specific physical execution. It uses human-centered command tokens to align full human motion, robot reference motion, and sparse VR observations; a causal Transformer learns reusable temporal structure from mixed multi-robot rollouts, and lightweight robot-specific modules map the shared representation to each robot's proprioception and action space.
arXiv cs.RO ·
AgiBot Team Releases GE-Act 2.0 World-Action Model Technical Report
The AgiBot Research Team released a technical report on GE-Act 2.0, a world-action model whose generative and action components are all trained from scratch on manipulation data. It consists of a control-oriented autoencoder (CoAE), a single-step visual planner (SVP), and an inverse dynamics model (IDM), and is jointly trained with KASO.
arXiv cs.RO ·
RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions
The research team proposes the RynnVLA-Latent framework, which uses RynnLAM to learn continuous, 3D-grounded latent actions from visual changes without native robot control signals, converting 30.1K hours of human, robot, and simulation video into 745.52M latent action descriptors for policy pretraining.
AgiBot ·
AgiBot releases Genie Studio 2.0 embodied intelligence development and deployment platform
AgiBot has released Genie Studio 2.0, an embodied intelligence application platform covering development, deployment, and operations. It consists of three modules—Flywheel, Agent, and Monitor—that connect data collection, model training, evaluation, inference, task orchestration, and robot fleet management.
机器之心 · 微信公众号 ·
RoboCoach: Using World Models to Locate Weak Subtasks and Improve Long-Horizon Task Success Through Targeted Teaching
Zhang Changshui and Cui Sen's team at Tsinghua University propose RoboCoach, an embodied intelligence system that uses the world model CoachWorld to run closed-loop trials of long-horizon tasks in imagination, identify the first weak subtask that times out, and request targeted human teaching for it, updating only the corresponding skill expert. Under a fixed demonstration budget, the success rate rises from 13.3% to 75.0% on Franka and from 40.0% to 83.8% on AgileX, while the uniform collection baseline reaches only 30.0% and 47.5%, respectively. On four unseen new task routes, the average success rate is 35.0%, compared with 0% for the shared-policy baseline.
HuggingFace Daily Papers ·
Rollback: Using World-Model Backtracking of Counterfactual Experience to Improve Robot Policies
Rollback proposes a policy improvement paradigm that turns an action-conditioned world model from a one-way simulator into a backtrackable counterfactual engine: it returns to key decision points, branches to try different actions, and compares the outcomes, thereby distilling counterfactual results into action-level supervision without learning a separate value model.