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
SIEVE: Structure-Aware Data Selection for Imitation Learning with VLAs
SIEVE introduces structure-aware data selection specifically for imitation learning with Vision-Language-Action models, aiming to improve training efficiency…
- Embodied AI 101
DexMachina: RL for Long-Horizon Bimanual Dexterous Policies
An RL algorithm that learns long-horizon bimanual dexterous policies for any robot hand from a single human demonstration, emphasizing generalization across…
- Embodied AI 101
Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation
A unified platform comprising GE-Base (video diffusion model trained on 1M+ manipulation episodes), GE-Act (flow-matching action model), and GE-Sim (neural…
- Embodied AI 101
RoboTTT: Test-Time Training for Visuomotor Policies
Introduces test-time training (TTT) inside the policy to natively scale visuomotor context to 8K timesteps at constant inference cost, enabling one-shot…
- Embodied AI 101
HY-Embodied-VLM-1.0: Efficient Physical-World Agents
An embodied vision-language-action model with released weights, code, and paper targeting robotic manipulation and embodied AI tasks.
- Embodied AI 101
Learning Unified Force and Position Control for Legged Loco-Manipulation
Introduces a unified RL policy that jointly handles force and position control on quadrupedal and humanoid robots without force sensors, enabling…
- Embodied AI 101
HapticVLA: Extending Vision-Language-Action Models to Contact-Rich Tasks Without Touch Sensors
Enables contact-rich robotic manipulation using a VLA model trained with tactile sensing data but requiring no tactile input at inference time. Distills haptic…
- Embodied AI 101
AnoleVLA: Lightweight VLA with Deep State Space Models for Mobile Manipulation
Proposes a lightweight VLA model leveraging deep state space models (SSMs) instead of transformers for efficient mobile manipulation. Targets…
- Embodied AI 101
ABot-N1: Visual Language Navigation Foundation Model
Decouples cognition from control in a VLM-based navigation policy, delivering 35% POI arrival gains and over 92% success rates in complex indoor and outdoor…
- Embodied AI 101
ABot-AgentOS: A General-Purpose Robotic Agent Operating System
Provides scene-conditioned planning, context-isolated skill execution, multi-modal memory, and self-evolution capabilities for long-horizon embodied tasks.
- Embodied AI 101
Trust Region Policy Distillation (TOP-D)
Transforms unstable on-policy distillation into a stable training paradigm via dynamic proximal teacher construction, improving sample efficiency without…
- Embodied AI 101
HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
A new humanoid whole-body control method that distills complementary teachers for agentic task-space control on humanoids. The approach enables robust…
- Embodied AI 101
EVA-Client: A Unified Framework for Real-Robot Policy Iteration
A unified open framework for real-robot policy iteration that integrates teleoperation data collection, model training, deployment, and evaluation into a…
- Embodied AI 101
UniVR-34B: A Vision-Only Foundation Model for Physical Tasks
First large-scale model to learn complex physical dynamics, visual reasoning, and long-horizon planning directly from visual demonstrations without text…
- Embodied AI 101
CLAP: Converting Vision-Language Models into Vision-Language-Action Models via Language-Prompted Actions
Converts any pretrained vision-language model into a vision-language-action model with zero architectural changes by prepending natural-language action…
- Embodied AI 101
InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action
InternVLA-A15 is a new Vision-Language-Action (VLA) model released by InternRobotics, targeting robotic manipulation and control tasks. The model is…
- Embodied AI 101
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
Proposes foundation world-action models targeting real-time humanoid loco-manipulation, integrating world modeling with action generation for unified…
- Embodied AI 101
On-Device Diffusion Transformer Policy for Efficient Robot Manipulation
LightDP introduces a framework to accelerate Diffusion Transformer-based policies for real-time, on-device deployment in robot manipulation tasks, achieving…
- Embodied AI 101
Robotic World Model: Neural Dynamics for Locomotion
A neural dynamics world model paired with model-free RL for quadruped and humanoid locomotion in IsaacLab, enabling long-horizon autoregressive prediction with…
- Embodied AI 101
LingBot-Vision: Self-Supervised ViT with Masked Boundary Modeling
A self-supervised ViT backbone pretrained with masked boundary modeling for dense spatial perception, achieving strong zero-shot results on depth estimation…
- Embodied AI 101
LaMem-VLA: Latent-Memory-Native VLA Framework
A VLA framework that curates experience into short- and long-term memory vaults, condenses them to latent tokens, and integrates directly into VLA reasoning…
- Embodied AI 101
LingBot-VA 2.0: Native Video-Action Foundation Model for Robot Control
A native video-action foundation model pretrained from scratch with a semantic visual-action tokenizer and foresight reasoning, enabling real-time robot…
- Embodied AI 101
GigaWorld-1: Large-Scale World Model for Robot Policy Evaluation
A large-scale world model trained on 12,980 hours of data and benchmarked across 324,000+ simulated rollouts, designed for comprehensive robot policy…
- Embodied AI 101
Learning Unified Force and Position Control for Legged Loco-Manipulation
Proposes a unified RL policy trained in simulation that jointly handles force and position control without force sensors, enabling compliant behaviors and…
- Embodied AI 101
Robix: Unified Vision-Language Model for Robotic Reasoning and Planning
A single vision-language model that unifies high-level reasoning, long-horizon planning, and human-robot interaction, serving as a cognitive layer above…
- Embodied AI 101
EmbodiedOneVision (EO-1): A Unified Decoder-Only Transformer for General Robot Control
A 3B-parameter open-source unified decoder-only transformer for general robot control that jointly handles perception, planning, reasoning, and action via…
- Embodied AI 101
mimic-video: Video-Action Models for Robot Learning
Introduces Video-Action Models (VAMs) that replace standard VLMs in VLAs with a pretrained internet-scale video backbone (Cosmos-Predict2) combined with a…
- Embodied AI 101
Lowering the Barrier: The GEM Open-Source Arm
An open-source, sub-$500 7-DOF 3D-printed robotic arm with 1.2 kg payload, wrist and head cameras, and full LeRobot integration, designed to lower the barrier…
- Embodied AI 101
RynnWorld-4D: A 4D Embodied World Model for Bimanual Robot Control
A 4D embodied world model that predicts RGB, depth, and optical flow from RGB-D input and instructions using a tri-branch diffusion architecture. Enables…
- Embodied AI 101
RynnWorld-Teleop: Digital Teleoperation via World Model Rendering
A digital teleoperation system that uses hand-pose streams to drive a world model for real-time (40+ FPS) high-fidelity robot video rendering from a single…
- Embodied AI 101
VLA-Corrector: Adaptive Action Horizons through Latent Visual Monitoring
A lightweight plug-in that monitors latent visual dynamics in action-chunked policies, drops stale actions on drift, and replans on-the-fly achieving +17.7…
- Embodied AI 101
Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization
A feed-forward model that decomposes unposed images into instance-structured 3D token groups without annotations, enabling unified reconstruction…
- Embodied AI 101
Current as Touch: Proprioceptive Contact Feedback for Compliant Dexterous Manipulation
A proprioceptive contact feedback method leveraging motor current signals as a touch proxy to enable compliant dexterous manipulation without dedicated tactile…
- Embodied AI 101
DART: One-Shot VLA Policy Adaptation via Weight-Space Arithmetic
Uses weight-space arithmetic to isolate domain shifts from task knowledge, enabling one-shot VLA policy adaptation to new cameras or embodiments.
- Embodied AI 101
Does VLA Even Know the Basics? Act2Answer Benchmark
Introduces a benchmark showing VLAs lose 20–40 points in commonsense/world knowledge versus source VLMs after robotics fine-tuning, evaluated via action-based…
- Embodied AI 101
Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Learns dexterous manipulation policies that explicitly ground actions in generative contact predictions from visuotactile observations, improving robustness on…
- Embodied AI 101
Freeform Preference Learning (FPL) for Robotic Manipulation
Introduces multi-axis preference supervision to learn dense, language-conditioned rewards across speed/precision/subtask axes without segmentation; enables…
- Embodied AI 101
Orca: The World is in Your Mind
Proposes a general world foundation model leveraging Next-State-Prediction to jointly generate text, images, and embodied actions within a unified framework.
- Embodied AI 101
Qwen-RobotNav: A Scalable Unified Navigation Model for Agentic Robotics
A unified 2B–8B parameter model for robot navigation tasks (VLN, ObjectNav, tracking, autonomous driving) via a configurable observation protocol, with…
- Embodied AI 101
Scaling Robot Skills from Cheap Human Videos
Replaces noisy 6-DoF hand poses with relative wrist translation as a shared action space between humans and bimanual robots, enabling scalable skill…
- Embodied AI 101
ABC: An Open Behavior Cloning Stack for Bimanual Manipulation
Large-scale open-source framework for real-world robotic manipulation using behavior cloning, including the ABC-130K dataset with 3,500 hours and 130K+…
- Embodied AI 101
Qwen-RobotManip: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
Presents alignment techniques to scale robotic manipulation foundation models, building on the Qwen model family for dexterous robot control.
- Embodied AI 101
ASPIRE: Automated Skill Discovery for Robotics
Introduces the first automated system that continuously discovers, evolves, and accumulates reusable sensorimotor skills via evolutionary search over control…
- Embodied AI 101
SERF: 4D Latent Mapping for Long-Horizon Mobile Manipulation
Embeds both the robot and environment into a shared 4D latent space augmented with forward-kinematics robot points, enabling a vision-language-action model to…
- Embodied AI 101
ViserDex: Visual Sim-to-Real for Robust Dexterous In-Hand Reorientation
A single-camera sim-to-real framework that uses physically consistent 3D Gaussian Splatting augmentations to achieve zero-shot transfer of dexterous in-hand…
- Embodied AI 101
DexSkin: A High-Coverage, Conformable "Electronic Skin" for Robot Fingers
Introduces a high-coverage, conformable robotic skin hardware system designed to improve data collection and policy learning for contact-rich, dexterous…
- Embodied AI 101
EBench: A Diagnostic Benchmark for Generalist Manipulation Policies
A CAT-scan style diagnostic benchmark for robot foundation models that evaluates policies such as π0, π0.5, and Qwen-RobotManip beyond single success rates…
- Embodied AI 101
VITRA: A Foundation for Dexterous VLA via Human Video Pretraining
A scalable VLA pretraining pipeline that converts unstructured egocentric human videos into robot training data, trains a dexterous hand VLA, and fine-tunes on…
- Embodied AI 101
DexWM: A Dexterous Manipulation World Model from Human Videos
A dexterous manipulation world model pretrained on 829 hours of EgoDex human data and DROID robot data using conditioned diffusion transformers, enabling…
- Embodied AI 101
PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Introduces PoLAR, a method that factorizes latent action representations into extent and mode components to improve robot policy learning efficiency and…
- Embodied AI 101
Continual Robot Policy Learning via Variational Neural Dynamics
Proposes a variational neural dynamics framework for continual robot policy learning, enabling robots to acquire new skills without forgetting previously…
- Embodied AI 101
PhysisForcing: Physics-Reinforced World Models for Robotic Manipulation
Plug-and-play training framework that enforces physical plausibility in robotic video generation models, achieving SOTA on R-Bench, PAI-Bench, and EZS-Bench…
- Embodied AI 101
Translation as a Bridging Action
Replaces noisy 6DoF hand poses with relative wrist translation as a shared action space between cheap human videos and bimanual robots. Scales data-efficiently…
- Embodied AI 101
Play2Perfect: Dexterous Play Pretraining for Precise Assembly
Pre-trains a dexterous hand via unstructured 'play' interactions with objects, then fine-tunes for precise assembly tasks including 0.5 mm clearance insertions…
- Embodied AI 101
Dexora: Open-Source VLA for High-DoF Bimanual Dexterity
First open-source Vision-Language-Action (VLA) model for dual-arm, dual-hand 36-DoF dexterous manipulation, trained on 100K simulated and 10K real trajectories…
- Embodied AI 101
WorldVLA: Towards Autoregressive Action World Model
Unifies VLA and world-modeling in a single autoregressive transformer that predicts both future images and actions. Outperforms separate VLA or world models on…
- Embodied AI 101
HumDex: Humanoid Dexterous Manipulation Made Easy
HumDex targets humanoid dexterous manipulation, aiming to simplify the development of dexterous manipulation capabilities for humanoid robots.
- Embodied AI 101
ForceBand: Learning Forceful Manipulation with sEMG
Presents an open-source, low-cost sEMG wristband framework that extracts force signals from human muscle activity in videos, enabling zero-shot human-to-robot…
- Embodied AI 101
In-Context World Modeling for Robotic Control
Introduces ICWM, a method that learns world dynamics from just seconds of a robot's self-generated interaction data, enabling zero-shot adaptation to unseen…
- Embodied AI 101
WOLF-VLA: Vision-Language-Action for Humanoid Walking
Introduces a framework integrating vision-language-action models for whole-body humanoid locomotion, addressing optimal control and learning for complex…