0:35Lyra 2.0: Explorable Worlds from One Image
@NVIDIAAI · 17 views · 2026-11-06
Lyra 2.0NVIDIA
Robots and embodied AI
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Latest Videos
16280:35
0:45EMG-1 Pairs Wrist EMG with Egocentric Video
@ulrikstighansen · 13 views · 2026-11-06
EMGMultimodal Learning
0:42RoboKit Reports up to 240x Faster Kinematic Optimization
@robotsdigest · 10 views · 2026-11-07
KinematicsRobot simulation
0:45MuJoCo 4.0 Improves Thin-Object and Soft-Body Simulation
@mujoco · 10 views · 2026-11-07
Physics simulationRobotics
1:26InstantHMR Brings Live Human Mesh Recovery to ComfyUI
@jtydhr88 · 15 views · 2026-11-07
Human Mesh RecoveryReal-Time Perception
3:07PIVOT Turns Deployment Experience into Better Bimanual Policies
@fancy_yzc · 18 views · 2026-11-07
ManipulationBimanual
0:52A 100,000-Object Library Defined in Code
@tivollem · 14 views · 2026-11-07
3D AssetsProcedural Modeling
1:36Human2Any Transfers Human Video Knowledge to Robots
@ShuoCheng94 · 15 views · 2026-11-07
Human VideoRobot Learning
Humanoid Showcase
257
ENGINEAI SE01
EngineAI
Boston Dynamics Atlas (Next-Gen Electric)
Boston Dynamics 波士顿动力
AiMOGA Mobo
奇瑞墨甲机器人 AiMOGA
AiMOGA Mornine M1
奇瑞墨甲机器人 AiMOGA
G2 Max
AgiBot humanoid guide 300x720
ESTUN COZROID CODROID 02 Embodied Humanoid Robot
埃斯顿酷卓科技（埃斯顿集团）ESTUN COZROID
DroidUp Moya Fully-Biomimetic Humanoid
卓益得 DroidUp
Unitree H2 EDU Humanoid Robot
宇树科技 Unitree
Creative Workshop
7
RetouchPi
Fifty-four professional tools, layers and masks, PSD files that open and save back - all in the browser, nothing to install.
retouchpi.com · 54 pro tools · PSD read and write · AI generate & edit
ThreeDream simulation engine
Built for games and for physical AI: an ultra-realistic, ultra-high-performance simulation environment with ultra-low-cost procedural data; Unreal-grade visuals open and play in the browser, bridging the physical world and intelligent agents.
Games meet physical AI · Unreal-grade on the web · Open and play
CodeWaifu, your caring coding desktop sprite
A caring coding sprite on your desktop and a cockpit that never stops: the work resumes itself after a crash or a reboot. Every coding terminal, ssh session and local shell in one tree, every start, finish and permission request reported in time. Open source, one-line install.
Your caring coding desktop sprite · Resumes after crash & reboot · First-rate terminal / ssh / shell
Agent digital twin office
A 100 km 3D city that opens in seconds — crawling, blogs, papers, industry and investment analysis, all watchable in one digital twin.
100km city · opens in seconds · Live task stream · Agent-maintained
Robotic arm workbench
An SO-101 6-DoF arm running MuJoCo physics in your browser: joint teleoperation, IK end-effector dragging and gripper pick-and-place into a basket. No install.
MuJoCo WASM physics · SO-101 · 6 DoF · Contacts / telemetry HUD
Joint actuator anatomy lab
Take a humanoid joint module apart layer by layer: brushless motor, magnetic encoder, planetary / harmonic / cycloidal drive and output flange. Switch architectures on one page — exploded view plus analytic kinematics.
Three gearbox types · Planetary / harmonic / cycloidal · Analytic kinematics · explode
Speech synthesis lab
Matcha-TTS mixed zh/en speech synthesis: server-side synthesis with sentence-streamed playback and full-article read-aloud for papers and blogs. Sign-in required.
Matcha-TTS · server-side · Mixed zh / en · Full-article read-aloud
Frontier papers
522
Social Humanoid: Socially Responsive Human-Humanoid Interaction
Humanoid robots are typically trained as solitary agents, leaving them unable to coordinate physically with human partners who naturally anticipate and adjust to one another. To bridge this gap, we present Social Humanoid, a hierarchical system that generates and executes socially responsive whole-body motion on a 29-DoF Unitree G1 conditioned on a human partner's motion. Its high-level policy performs flexible sequence modeling of human and humanoid motion through a shared tokenized representation, generating reactive humanoid motion and anticipating the partner's motion when they are occluded, while a low-level whole-body tracking controller executes the generated motion on the robot. To train the system, we introduce a two-phase adaptive retargeting procedure that retargets one interaction partner to the robot while morphing the other partner to preserve contact dynamics, reachability, and relative body scale. We apply this procedure across five motion-capture datasets to produce a new Human-Humanoid Interaction (HHI) benchmark of 260 hours of human-humanoid interactions. Our results demonstrate that Social Humanoid produces coordinated, human-aligned behavior across diverse interaction types in simulation and the real world. Project page: https://von31.github.io/social-humanoid/
Vongani H. Maluleke, James Ni, Kie Horiuchi · 68 views · Nov 5, 2026
HumanoidHuman-humanoid interaction
Nov 5, 2026
OmniBrain-0: A Unified Agentic Model for Embodied Intelligence
General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures. Specialist policies alone do not ensure task-level reliability, while powerful multimodal agents rely on costly reasoning and external experience that compact models struggle to reproduce. We introduce OmniBrain-0, a lightweight unified embodied agent that coordinates manipulation and navigation through a shared high-level decision model, integrating task understanding, planning, progress tracking, and feedback-driven recovery. Our progressive training framework combines embodied pretraining, domain-specialized agent distillation, and multi-teacher on-policy distillation (MOPD). It distills powerful teachers' interaction experience into manipulation and navigation experts, then consolidates their decision capabilities into OmniBrain-0. Unification therefore occurs within the learned decision model rather than only at the tool interface. We further introduce Rubric-Guided Recursive Self-Improvement (RSI), a data flywheel in which a Meta-Agent turns execution evidence into refined rubrics, step-level reminders, and verified trajectories for domain-expert updates before their reconsolidation into OmniBrain-0 through MOPD. We evaluate OmniBrain-0 on LIBERO-Pro, LIBERO-Plus, RoboCasa365, RoboTwin 2.0, and R2R-CE Val-Unseen, as well as multiple real-world manipulation tasks. OmniBrain-0 combines strong cross-domain task performance with substantially lower inference and execution overhead than its closed-source teacher, demonstrating efficient transfer and consolidation of embodied capabilities into a compact unified agent.
Xiaomi Embodied Intelligence Team, Yuechen Luo, Chong He · 104 views · Oct 26, 2026
Embodied AIVLA
Oct 26, 2026
One Scan is Enough: Demonstration-Free Adaptation for Language-Guided Navigation
Deploying a language-guided navigation policy in a new building usually means watching it fail. Public-benchmark VLAs degrade scene by scene as layout, appearance, and language drift, and the standard fix of teleoperating fresh trajectories on site does not scale. We replace human demonstration with a single brief iPhone scan. Scan2Instruction compiles that scan into a complete training environment, automatically synthesizing executable navigation episodes with grounded instructions. PRPT-KS then adapts the policy through a closed-form Pareto utility read directly off the reconstructed scene, with no learned critic and no human labels. Across five real environments, the two designs together drive our RealNav to an 85% real-robot success rate, compared with 49% for a RealNav baseline post-trained on human demonstrations, and still achieve 82% in unscanned regions of the same buildings, compared with 41% for the same baseline. Our work turns demonstration-free scene adaptation for language-guided navigation from an aspiration into a working reality.
Zhuoyuan Yu, Yuxing Long, Du Wang · 70 views · Oct 24, 2026
Vision-language navigationRobot navigation
Oct 24, 2026
GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation
Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks remains challenging. Task success often hinges on subtle, view-dependent visual cues, while task-level rewards provide little guidance about which regions matter, making it difficult to learn task-relevant visual grounding from limited real-robot interaction. Online adaptation is further constrained by the computational cost of VLA inference and replay-based updates. We introduce GRAFT (Grounded Reinforcement Adaptation for Fast Task Learning), a framework for efficient online VLA adaptation through grounded perception. GRAFT uses region-level supervision to learn view-specific visual anchors that focus perception on task-relevant local cues without requiring region proposals at deployment. It further combines single-step action generation with cached visual-language prefix reuse to accelerate online learning. Across four biomedical manipulation tasks, GRAFT improves success rates by 32.5 percentage points under matched adaptation budgets, while reducing the computational overhead of online policy updates.
Yibo Qiu, Haoliang Ye, Shu'ang Sun · 95 views · Aug 27, 2026
VLARobot Manipulation
Aug 27, 2026
RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments
RobotWorld introduces a cross-embodiment robot-use benchmark spanning 84 tasks and evaluates whether multimodal agents can convert perception and computation into verifier-confirmed physical execution.
Zhiqin Yang, Chenxin Li, Xiaomeng Hu · 89 views · Oct 7, 2026
Robot BenchmarkEmbodied AI
Oct 7, 2026
Hybrid Computer Use: A Unified Programmatic, Semantic, and Visual Interface for General Agents
The paper introduces Hybrid Computer Use (HCU), a unified agent interface that combines code and command-line interaction, accessibility-tree semantics, and screenshot-based mouse and keyboard control in one context. Compact observations, batched action execution of up to 64 steps, wait steps, post-action diffs, and cross-channel goal and layout guards reduce round trips and prevent stale-input actions. HCU improves task success or interaction efficiency across OSWorld 2.0, OSWorld 1.0, WeaveBench, macOS, and Windows Agent Arena.
Haoqing Wang, Xiaoyi Zhang, Sicheng Fan · 79 views · Oct 30, 2026
Agent HarnessComputer Use
Oct 30, 2026Blog articles
115
The Sleep Framework: A New Way for LLMs to Retain Knowledge in Continual Learning
Google Research separates fast context, low-rank expert consolidation, and synthetic dreaming to improve continual learning without simply enlarging the active context window.
Continual learningAgent Memory
The Components of Agent Memory
Anthropic lays out a four-layer agent-memory hierarchy and shows how dreaming improves recall, corrects errors, and builds navigable knowledge.
Agent MemoryContext Engineering
Agents Are Going Async: Orchestrators, Background Workers and the Long-Running Task
LangChain separates an always-responsive orchestrator from background subagents, using stable task IDs, notifications, approvals and steering to make long-running agent work concurrent and controllable.
AI AgentAgent Architecture
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soundcore
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