Trains a world-action model that predicts future visual states to guide zero-shot robotic manipulation; introduces CoAE, SVP, IDM and KASO to pretrain generative and action components from scratch on manipulation data, scaling up to 30,000 hours and improving zero-shot success.
Generates compositional 3D scenes as collections of individual object meshes by conditioning a single-object 3D generative prior on multi-view posed observations. Key features include anchor-aligned canonical frames, multi-view DINOv3 feature lifting with an IBR-style fusion, and LoRA adaptation to complete heavily occluded objects; includes a large UE-MeshyScene benchmark.
AI Model2026
Multimodal foundation model for visual understanding, spatial reasoning and multi-step agent tool use — accepts text, multiple images and video at any resolution and supports long contexts (up to 128K tokens). Emphasizes fine-grained 2D/3D relations, affordance reasoning and embodied-AI planning.
Trains end-to-end driving without human trajectory supervision by decoupling perception and action: DriveVFM distills multiple frozen vision foundation models into a single camera backbone, and DriveRL trains a privileged closed-loop RL teacher whose rollouts supervise a camera-only planner, yielding state-of-the-art closed-loop benchmark results.
Trains LVLMs to improve 3D spatial reasoning using a synthetic block-stacking dataset of 15,000 problems that cover 3D→2D projection, viewpoint transforms, and structural combination; includes color-anchored cues and provides code and data.
AI Agent Papers2026
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.
AI Dataset2026
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.
AI Agent Papers2026
Defines recursive self-improvement (RSI) with an evaluation metric (Headroom-Closed Index) and a five-level autonomy roadmap—from executing human-specified improvements to recursively improving improvement mechanisms—while analyzing scenarios, verification challenges, and governance risks.
AI Dataset2026
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.
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.
AI Dataset2026
Provides a complete benchmark and training release for native audio‑visual dialogue: 2,800 synthesized audio‑visual dialogues, tiered rubrics, scoring code, ~28GB of 1080p media, and an RL reward recipe to evaluate and train omni models that take video+audio and return text.
AI Dataset2026
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.