Embodied AI 101

Introduces dual-brain fusion Vision-Language-Action model with LangForce physics-grounded training methodology.

Vision-Language-Action (VLA) model fine-tuned on the merged LIBERO robotics dataset (1,693 episodes, 273k+ frames) achieving 98.25% success rate on manipulation tasks. Released with both checkpoint and dataset for VLA finetuning.

A 2.6B-parameter open-source world model that generates coherent 720p, minute-long videos with precise 6-DoF camera control on a single GPU using a Hybrid Linear Diffusion Transformer + Gated DeltaNet for long-context efficiency. Targets controllable physics simulation.

New benchmark and dataset for robotic manipulation in unconstrained 'wild' environments. Includes standardized containers, leaderboards, and evaluation protocols for cross-embodiment policies.

Introduces a latent-space world model framework that lets agents simulate state transitions and iteratively refine plans before real-world execution. Evaluated on 20+ MCP-Bench tasks with measurable gains in tool-use success.

Details OpenAI's reasoning-focused o1 model and its 'long thought' approach using test-time compute scaling. Explores how extended reasoning during inference can improve model performance on complex tasks.

Comprehensive technical report on the Llama 3 family, covering architecture, training at scale, multimodal extensions, and real-world impact. Details the development of Meta's flagship open-source language model series.

Introduces a three-stage pipeline that extracts a latent action space from low-quality human tennis demonstrations, then trains a high-level policy in simulation via reinforcement learning. Enables dynamic whole-body humanoid tennis play with back-and-forth volleys at human level.

First any-step video diffusion framework using flow maps, allowing a single model to adapt to arbitrary inference budgets for scalable high-quality video generation relevant to predictive world modeling.

Technical roadmap mirroring LLM scaling: critiques VLAs, advocates video world models as second pretraining phase, introduces World Action Models (WAM), manipulation data flywheels, EgoScale with new Dexterity Scaling Law, and DreamDojo end-to-end neural physics engine for sim RL.

Benchmark with 2,159 rubric items across 300 tasks using trajectory-aware grading and 3-trial Pass^3 scoring to mitigate luck. Evaluates agent reliability in real-world robotics settings.

Reveals paraphrase fragility in VLAs causing 22-52% success drops due to task misidentification. Introduces PRIDE metric weighting success by paraphrase difficulty on LIBERO benchmark manipulation tasks.

Low-cost mobile manipulator design and training strategies for broad generalization in real-world tasks.

Closed-loop egocentric video simulator maintaining persistent 3D scene state for consistent interactions, enabling cross-embodiment transfer from human videos to robotic manipulation.

Comprehensive survey taxonomizing efficient architectures/algorithms for video world models as simulators, targeting compute bottlenecks in embodied AI, autonomous driving, and games with techniques like short-window attention for real-time long-horizon prediction.

Curated collection of 100+ works surveying shift to continuous latent spaces in LLMs/VLMs/VLAs for improved reasoning over discrete tokens, with relevance to robotics action modeling.

CaP-X is an open-source agentic robotics framework where LLMs/VLMs generate code to call perception and control APIs for execution across diverse simulated and real robots in CaP-Gym's 187 manipulation tasks. The framework includes CaP-Bench for evaluating frontier models and CaP-RL, which boosts a 7B model's success from 20% to 72% with minimal sim-to-real gap.

An upgrade over LoRA for parameter-efficient fine-tuning, enabling better performance in LLMs by decomposing weights into magnitude and direction components.

Seminal work showing how training on AI-generated data leads to 'model collapse' in neural networks, with urgent implications for future scaling.

Real-world hardware evaluation of VLAs on blind bin-to-bin picking, achieving max 64 picks/hour across hundreds of runs, with full videos/data exposing gaps in production-scale robotic manipulation reliability.

Comprehensive evaluation of 89 policies showing optimal co-training practices mixing real robot data with sim/egocentric human videos to boost diversity and performance in large robotics foundation models.

Novel memory system preserving dynamic object identity and motion continuity across occlusions in video world models, addressing frozen/vanishing issues for improved predictive physics in embodied AI.

Massive dataset enabling dynamic world models with explicit states and actions, supporting predictive modeling for cross-embodiment robotic control.

New benchmark assessing world models on interaction tasks, pushing predictive physics and video modeling towards robotics applications with action-conditioned evaluation.

Unified MLLM framework with Sparse 3D VQ-VAE (70% token reduction) for part-level mesh decomposition and kinematic chain prediction, enabling physics-based robotic simulation from monolithic assets.