Shows / Embodied AI 101
Embodied AI 101
Embodied AI 101 tracks the latest research at the intersection of AI and physical intelligence. Robotics, manipulation, world models, and the path from digital intelligence to embodied agents — each episode breaks down recent papers and advances in the field. We examine the methodology, real-world implications, and path to deployment. Covering robot learning, perception, control, and the fundamental challenge of grounding intelligence in the physical world. New episodes daily.
Episodes
- Embodied AI 101
High-Level Automated Reasoning with Qwen2.5-7B
Qwen2.5-7B achieved 79.6% on MATH benchmark, surpassing GPT-4o, by employing atomic reasoning actions combined with Monte Carlo Tree Search. Demonstrated that…
- Embodied AI 101
Co-Training Large Behavior Models: Multimodal Data for Robot Manipulation
Explores data modalities and co-training strategies to enhance large behavior models (foundation models) for improved performance in robot manipulation tasks…
- Embodied AI 101
HyDRA: Hybrid Memory for Dynamic Video World Models
Memory architecture preserving identity and motion continuity for out-of-view dynamic subjects, addressing frozen/vanishing issues in video world models.
- Embodied AI 101
DexWM: Leveraging Human Videos for Dexterous Robot World Models
Dataset of robot trajectories designed for training world models to learn dexterous hand-object interactions directly from human videos.
- Embodied AI 101
World Models in Robotics
Technical survey categorizing world models into action-conditioned, video-inverse dynamics, and joint world-action models (WAMs), discussing their…
- Embodied AI 101
SIMART: Decomposing Monolithic Meshes into Sim-Ready Articulated Assets
Unified MLLM framework with Sparse 3D VQ-VAE that reduces tokens by 70% for efficient part-level decomposition and kinematic prediction in physics-based…
- Embodied AI 101
LeWorldModel: A Stable JEPA World Model from Pixels
Stable end-to-end JEPA world model trained directly from pixels using simple MSE prediction loss and SIGReg anti-collapse regularization, enabling efficient…
- Embodied AI 101
World Models for Robots: The Next Big Leap?
Technical overview defining world models in robotics, their potential to solve diverse problems via video prediction, and key enablers like scale.
- Embodied AI 101
Harnessing Long-Running AI in Embodied Systems
As AI moves from quick Q&A to marathon tasks, designers grapple with continuity. This episode explores how Anthropics harness design principles translate to…
- Embodied AI 101
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
Whole-Body Mobile Manipulation Interface (HoMMI) that learns bimanual and whole-body manipulation, long-horizon navigation, and active perception directly from…
- Embodied AI 101
TurboQuant: Redefining AI Efficiency with Extreme Compression
This episode explores TurboQuant, a revolutionary set of quantization algorithms from Google Research that redefines AI efficiency through extreme compression…
- Embodied AI 101
DexWM: Learning Dexterous Object Manipulation from Human Videos
Dataset of robot trajectories designed for training world models that learn dexterous hand-object interactions from human videos, released on Hugging Face.
- Embodied AI 101
FlashAttention-3: Fast & Accurate Attention with Asynchrony & Low-Precision
Major efficiency leap for Transformer attention mechanisms, enabling faster training/inference on long sequences with low-precision compute.
- Embodied AI 101
When AI Trains on Its Own Output: The Model Collapse Problem
Warns of "model collapse" in LLMs trained on synthetic data from prior models, urging preservation of human-generated data. One of 2024's most influential…
- Embodied AI 101
MolmoBot: A Vision-Language Model for Zero-Shot Robot Manipulation
Vision-language model (VLM) for zero-shot robot manipulation, trained entirely in simulation without real-world data; achieves 79.2% success rate on real-world…
- Embodied AI 101
LeWorldModel: Stable End-to-End JEPA from Pixels
A stable end-to-end Joint Embedding Predictive Architecture (JEPA) trained directly from pixels that enables robust world modeling for embodied AI systems.
- Embodied AI 101
EgoVerse: An Egocentric Data Ecosystem for Scaling Robot Learning
Ecosystem with over 1300 hours of egocentric human video data spanning 240 scenes and 2000+ tasks, designed for scalable robot policy training via behavior…
- Embodied AI 101
HSImul3R: Physics-Driven Reconstruction of Human–Scene Interactions
Physics-in-the-loop bi-directional optimization pipeline reconstructing stable, simulation-ready 3D human-scene interactions from casual videos, deployable…
- Embodied AI 101
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Open-source suite of large-scale simulation environments and benchmarks designed for advancing end-to-end learning in robot navigation and manipulation across…
- Embodied AI 101
DreamZero: World Action Models Are Zero-Shot Policies
Introduces World Action Models (WAMs), a family of 14B-parameter autoregressive diffusion models that jointly predict video and robotic actions to enable…
- Embodied AI 101
Kinema4D: A 4D Generative Simulator for Embodied AI
An action-conditioned 4D generative robotic simulator that disentangles precise kinematic control from environmental dynamics, facilitating…
- Embodied AI 101
VEGA-3D: Teaching multimodal LLMs spatial reasoning through video generation
A plug-and-play framework extracts implicit 3D priors from video diffusion models to enhance multimodal LLMs with spatial reasoning capabilities, enabling…