Category

Show-Harness: Just a VLM Agent Can Play Robots

Lets vision-language models control robots via a compact semantic action interface that maps intent to discrete action units; supports zero-shot use of closed-source VLMs, low-cost fine-tuning of open VLMs, and GUI-based demonstration collection.

Yootta/World-SimReady-Home

Simulation-ready home dataset for embodied AI: CAD-based household scenes with configured physical properties and metadata, plus 1,000 robot trajectory episodes (RGB-D, HDF5/USDZ) for simulation training and evaluation under CC BY-NC-SA 4.0.

Eidon Tracker POV

Provides 1,274 hours of head-mounted egocentric video paired with seven-point IMU arm tracking (24 Hz orientation; raw accel/gyro/mag on a subset), packaged for embodied-AI and egocentric-vision research. Key features: torso-relative pose via chest reference, separate Parquet IMU repo for efficient joins, CC-BY-4.0 license; heavy class skew and limited contributor diversity are important constraints.

ActionPiece: Rethinking Action Tokenization for Autoregressive Vision-Language-Action Models

Introduces physical rank consistency (PRC) and ActionPiece, a learned action tokenizer that preserves local physical relationships via joint supervision of representation learning and quantization, improving autoregressive vision-language-action policy performance across multiple benchmarks.

HOI-Retarget — Humanoid Human–Object Interaction Motion

Generates humanoid robot motion references that preserve object contact locations/timing by solving windowed trajectory optimizations against contact targets in the object frame. Releases retargeted trajectories for two Unitree robots across 75 objects (≈13.9k robot–motion pairs); CC BY‑NC‑SA 4.0.

Transferring the Intelligence of VLMs to Robotic Control

Uses a compact discrete-action interface (translation, rotation, gripper) to let frozen vision–language models control robots in closed-loop, and leverages in-context (one-shot) demonstrations to substantially improve manipulation success without robot-specific training.

Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World

Teaches vision-language models to predict and integrate physical-world state transitions from interaction trajectories (observation → action → next observation). Introduces a three-level curriculum and the LSI-108K dataset, and applies supervised fine-tuning plus on-policy distillation to improve local transition modeling and long-horizon spatial integration.

OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents

Provides a unified benchmark and a spatially aware omni-modal model for spatial audio-visual reasoning in embodied agents — OmniEchoBench (197 scenes, 2,972 QA pairs; 900 FOA-based navigation tasks) plus OmniEcho, which adds an FOA spatial encoder to a pretrained semantic audio pathway to enable spatial QA and sound-guided navigation.

GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay

Provides a unified data and evaluation suite for measuring gameplay capabilities across short, medium, and long temporal horizons. Includes an automated annotator, a 5,000-hour AAA gameplay dataset with temporally aligned frames, actions and multi-horizon instructions, and a reproducible offline + stepwise online benchmark.

InternW0-$Δ$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data

Learns joint predictive visual dynamics and action generation for generalist robot manipulation, translating future-relevant visual representations into actions. Integrates a Mixture-of-Transformers coupling a video expert and action expert, a frozen vision-language model for semantics, 4D distillation, and Causal Imprint; pretrained on a 20K+ hour heterogeneous corpus.

PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation

Uses 360° panoramic observations to improve vision-and-language navigation by predicting longer action sequences, confidence-guided execution, and combined semantic–geometric panorama features; yields large SR gains on R2R-CE and RxR-CE Val-Unseen.

Agent Priors-guided Policy Learning

Records structural priors with skill-specific policies so a runtime agent can select and compose the version of each skill best suited to new states, improving out-of-distribution and compositional generalization for robot manipulation from few demonstrations.