#Zero-Shot (20)

Natura-1: 50B MoE Drives 20-DoF Dexterous Hands Zero-Shot From Plain Language

@stingning · 149 views · 2026-10-13

Navi-Agent: Unlocalized Monocular Navigation Agent

Vision-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to execute long-horizon instructions in unknown environments. Existing zero-shot VLN-CE systems typically maintain spatial states through geometric localization or coordinate-based representations. Recent geometry-constrained navigation removes depth and globally consistent coordinates, but maintaining persistent spatial awareness for place confirmation, progress verification, and recovery remains challenging. We present Navi-Agent, a zero-shot VLN-CE agent that constructs a coordinate-free spatial state from visual observations and executed motion histories. Navi-Agent organizes this state as a navigation topology, where nodes represent visual places and edges represent motion transitions. This representation enables observation-based approximate self-localization, task progress verification, and visual revisitation-based recovery. Navi-Agent performs closed-loop navigation by decomposing instructions into sub-goals, executing local visual navigation, and verifying visited places through the constructed spatial state. Experiments on zero-shot VLN-CE benchmark and real-world robot platforms show that Navi-Agent achieves state-of-the-art performance among geometry-constrained methods while remaining competitive with approaches relying on geometric localization.

Xie, Wenyuan, Hong, Mengyang, Wang, Yongzhong · 636 views · Sep 17, 2026

VLNVision-language navigationVisual navigationSep 17, 2026

1:06

Helix 2.5: zero-shot full-body behaviors in 30 real homes

@coreylynch · 834 views · 2026-09-17

HumanoidFigure AIHelix

0:09

Helix 2.5 Recap: Scaling Human Data to Zero-Shot Generalization

@CyberRobooo · 646 views · 2026-09-18

HumanoidFoundation ModelsData Scaling

Show-Harness: Just a VLM Agent Can Play Robots

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific interpreters deterministically ground them into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. Through the same interface, Show-Harness demonstrates the feasibility of (1) directly unlocking closed-source frontier VLMs for zero-shot robot control, and (2) adapting small-scale open-source VLMs for low-cost deployment with just a few GPU-hours of fine-tuning. We further develop GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms. These results suggest that the right interface can unlock substantial embodied capability from foundation VLMs, without requiring additional model capacity or costly embodiment-specific pretraining.

Yanzhe Chen, Zechen Bai, Zhijun Cao · 1k views · Sep 9, 2026

Vision-language modelsAgentic HarnessSemantic Action InterfaceSep 9, 2026

1:50

Introducing LightNav-0: Scaling Real2Sim2Real for Zero-Shot Generalist Navigation

Light Origins' Real2Sim2Real engine turns 2,000+ internet-sourced scenes into 4,000+ hours of aligned VLA experience. LightNav-0 tops 10 simulated settings and transfers zero-shot to humanoid, quadruped, aerial, and wheeled robots.

BLOG

Real2Sim2RealLight OriginsPoint CoTEmbodied AINavigation

ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control

Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittle process of tuning controllers. We introduce ZEST (Zero-shot Embodied Skill Transfer), a streamlined motion-imitation framework that trains policies via reinforcement learning from diverse sources – high-fidelity motion capture, noisy monocular video, and non-physics-constrained animation – and deploys them to hardware zero-shot. ZEST generalizes across behaviors and platforms while avoiding contact labels, reference or observation windows, state estimators, and extensive reward shaping. Its training pipeline combines adaptive sampling, which focuses training on difficult motion segments, and an automatic curriculum using a model-based assistive wrench, together enabling dynamic, long-horizon maneuvers. We further provide a procedure for selecting joint-level gains from approximate analytical armature values for closed-chain actuators, along with a refined model of actuators. Trained entirely in simulation with moderate domain randomization, ZEST demonstrates remarkable generality. On Boston Dynamics' Atlas humanoid, ZEST learns dynamic, multi-contact skills (e.g., army crawl, breakdancing) from motion capture. It transfers expressive dance and scene-interaction skills, such as box-climbing, directly from videos to Atlas and the Unitree G1. Furthermore, it extends across morphologies to the Spot quadruped, enabling acrobatics, such as a continuous backflip, through animation. Together, these results demonstrate robust zero-shot deployment across heterogeneous data sources and embodiments, establishing ZEST as a scalable interface between biological movements and their robotic counterparts.

Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu · 759 views · Jan 30, 2026

ZESTSim-to-RealAtlasJan 30, 2026

0:14

NanoOWL: Zero-Shot Real-Time Object Detection by NVIDIA

@simplifyinAI · 479 views · 2026-08-24

NVIDIANanoOWLZero-Shot

3:08

ADEPT: Sim Pre-training for Zero-Shot Visuo-Tactile Dexterity

@jayjunleee · 642 views · 2026-08-20

ADEPTDexterous HandDexterous Manipulation

0:18

Enigma puts 100+ robots under unscripted internet user commands

@roboindexio · 418 views · 2026-08-12

Zero-ShotEnigmaRobot foundation models

Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonlinear, hardware-dependent behavior of physical motors. While conventional approaches attempt to bridge this gap by increasing simulator fidelity through system identification, domain randomization, or learned actuator models, we introduce an alternative paradigm: actuator reality shaping. Instead of modifying the simulator to match the real world, our method shapes the closed-loop behavior of physical actuators to match the idealized second-order reference dynamics used in simulation. By equipping each joint with a two-degree-of-freedom feedforward–feedback controller, we decouple reference-response shaping from robust stabilization, thereby providing a standardized actuator interface for reinforcement learning policies. As a result, policies trained only with the prescribed reference model can be deployed zero-shot on real hardware without task-level fine-tuning or learned actuator models. We validate the approach on a single-joint high-gear-ratio servo under external loads and a 7-DOF robotic arm reaching task, where actuator reality shaping substantially reduces sim-to-real tracking error and improves zero-shot task performance compared with standard servo-control and representative real-to-sim-to-real baselines. We further demonstrate zero-shot transfer on a wheeled-legged robot driving over a slope and a humanoid robot walking, suggesting that actuator reality shaping can serve as a reusable interface for robot learning across diverse hardware platforms. Project page: https://syamamori.github.io/ActuatorRealityShaping.github.io/

Satoshi Yamamori, Koji Ishihara, Kenjiro Minamikawa · 381 views · Jul 2, 2026

Sim-to-RealZero-ShotActuatorsJul 2, 2026

Zero-Shot Mission-Level Evaluation for Aerial MLLM Agents

Multimodal Large Language Models (MLLMs) are emerging as core reasoning modules for embodied agents, yet it remains unclear how well general-purpose models can solve long-horizon embodied tasks from a single high-level instruction. We introduce MissionBench, a benchmark for mission-level evaluation of MLLMs in aerial 3D environments. It comprises 120 missions across five simulated 3D environments and four task families. Agents must autonomously plan, navigate, and report outcomes using only egocentric observations and its action history, without aerial-specific fine-tuning. Across 22 open- and closed-source MLLMs, the strongest model succeeds on fewer than 35% of missions compared to 84.4% human performance, highlighting the difficulty of multi-step embodied tasks. Despite large variations between model families, we observe gains from scaling, indicating that larger general-purpose models possess stronger zero-shot embodied capabilities. Our analysis shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception, including multi-step planning and adaptive reasoning. This motivates closed-loop evaluation and highlights both the promise and risk of scaling-driven improvements for embodied AI.

Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt · 361 views · Jul 24, 2026

AerialMLLMZero-ShotJul 24, 2026