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#CoRL 2026 (6)

WaterfallPagination
MessyMem: Persistent Spatial Memory for Robots
0:48

MessyMem: Persistent Spatial Memory for Robots

@anuvabanwasi · 162 views · 2026-09-16
Persistent MemoryMobile Manipulation3D scene graphs
EgoHTR: Egocentric 4D Human-Terrain Demonstrations
2:06

EgoHTR: Egocentric 4D Human-Terrain Demonstrations

@leggedrobotics · 127 views · 2026-09-15
Egocentric 4D DatasetHuman Motion CaptureTerrain Traversal
Assembling Two Parts in One Hand
0:20

Assembling Two Parts in One Hand

@heetezition · 149 views · 2026-09-11
In-Hand ManipulationDexterous AssemblyReinforcement Learning
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model

We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm placement, torso adjustment, and gait modulation for collision-free movement through complex 3D spaces. We introduce TANGO, the first whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments. Given a natural-language instruction and egocentric RGB observations, TANGO directly predicts 29-DoF joint-space actions for downstream whole-body control. We train TANGO entirely in simulation by synthesizing diverse collision-free traversal behaviors via global path planning, kinematic whole-body motion generation, obstacle-aware motion editing, and RL-based tracking. This pipeline provides dynamically feasible action supervision for learning language-conditioned whole-body policies. In extensive simulation experiments, TANGO demonstrates state-of-the-art performance in vision-language navigation, while outperforming strong modular baselines in navigating challenging scenes requiring obstacle negotiation. Lastly, we deploy TANGO zero-shot on a Unitree G1 humanoid robot, and observe robust language-guided traversal in cluttered real-world scenes without training on any real-world navigation data.

Anqi Li, Yuxin Chen, Zhaobo LiSep 8, 2026
HumanoidVLANavigationSep 8, 2026
SkillX: one RL policy that dribbles, traps and shoots on a real Noetix E1
2:30

SkillX: one RL policy that dribbles, traps and shoots on a real Noetix E1

@RunhanH · 280 views · 2026-09-09
Humanoid SoccerHumanoidReinforcement Learning
EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data

EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data

Egocentric human data offers scalable supervision for robot manipulation. However, behavior cloning entangles transferable content like objects, scenes, and task semantics, with non-transferable factors like human morphology, head motion, and behavioral style. We study whether World Action Models (WAMs) provide a better training signal by requiring policies to predict not only actions, but also how the scene evolves. The central question is what world representation best enables human-to-robot transfer. We hypothesize that an effective world target should abstract appearance, capture agent-invariant physical effects, and separate camera motion from environment change. We introduce EgoWAM, a controlled human-robot co-training framework that fixes the policy backbone, action head, and data mixture while varying only the world prediction target, comparing Pixel, DINO, and 3D motion flow. Across three real-world bimanual tasks, WAM co-training scales more effectively with in-the-wild egocentric human data than behavior cloning. Pixel-based prediction transfers weakly, while DINO and 3D flow yield substantial gains: DINO improves out-of-distribution object and scene generalization by up to 4x, and 3D flow improves in-domain performance by 20-30%. More details: https://gatech-rl2.github.io/egowam.github.io

Baoyu Li, Xinchen Yin, Mengying LinJul 8, 2026
World ModelsRobot ManipulationImitation LearningJul 8, 2026