Embodied AI 101

A dexterous manipulation world model pretrained on 829 hours of EgoDex human data and DROID robot data using conditioned diffusion transformers, enabling open-loop rollouts and sim-to-real transfer with minimal robot fine-tuning.

Introduces PoLAR, a method that factorizes latent action representations into extent and mode components to improve robot policy learning efficiency and generalization.

Proposes a variational neural dynamics framework for continual robot policy learning, enabling robots to acquire new skills without forgetting previously learned ones.

Plug-and-play training framework that enforces physical plausibility in robotic video generation models, achieving SOTA on R-Bench, PAI-Bench, and EZS-Bench. Lifts WorldArena success rate from 16% to 24% with zero extra inference cost.

Replaces noisy 6DoF hand poses with relative wrist translation as a shared action space between cheap human videos and bimanual robots. Scales data-efficiently and outperforms full-pose baselines on manipulation tasks.

Pre-trains a dexterous hand via unstructured 'play' interactions with objects, then fine-tunes for precise assembly tasks including 0.5 mm clearance insertions and furniture screwing, achieving 33x better sample efficiency than RL from scratch.

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 with strong cross-embodiment transfer capabilities.

Unifies VLA and world-modeling in a single autoregressive transformer that predicts both future images and actions. Outperforms separate VLA or world models on LIBERO simulation benchmarks.

HumDex targets humanoid dexterous manipulation, aiming to simplify the development of dexterous manipulation capabilities for humanoid robots.

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 transfer of forceful manipulation policies across any robot, camera, or environment.

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 cameras and new robot morphologies without any fine-tuning.

Introduces a framework integrating vision-language-action models for whole-body humanoid locomotion, addressing optimal control and learning for complex bipedal behaviors. Combines VLA learning with locomotion-specific control for humanoid robots.

Proposes a motion-focused latent action representation for cross-embodiment vision-language-action policies learned from human videos, accepted to IROS 2026.

ManiFlow is a visuomotor imitation learning policy using consistency flow matching with a DiT-X architecture that generates high-quality actions in 1–2 steps. It works across single-arm, bimanual, and humanoid platforms using RGB or point cloud inputs.

RL-100 demonstrates highly reliable real-world RL manipulation achieving 900/900 success rates across 7 tasks with up to 250 consecutive trials without failure. It also shows strong robustness to disturbances and zero/few-shot adaptation capabilities.

A video-diffusion world model trained on over 1 million manipulation episodes (3,000 hours) that includes an action model and neural simulator for closed-loop robotic manipulation control, with all code and models open-sourced.

Proposes a hierarchical imitation learning framework using bilateral control, subtask-level progress tracking, and keyframe memory to handle long-horizon, contact-rich manipulation tasks.

Uses reinforcement learning to improve process reasoning capabilities in robotic manipulation policies, shifting the model from passive observation to active critique.

ROVE leverages reinforcement learning to enable humanoid robots to benefit from human interventions during manipulation tasks.

A control framework for safe humanoid robot motion tracking using RL policies with real-time constraint enforcement via kinematics, dynamics, and control barrier functions.

Introduces spatio-temporal policy learning combined with physics-guided filtering to achieve robust and extremely agile robot control.

Introduces a tokenization-free autoregressive policy learning framework using flow matching across scales for robotic control.

Introduces a slow-fast imitation learning framework combining diffusion-based planning with reactive tactile/force feedback for contact-rich manipulation tasks. Also includes TactAR, an AR-based teleoperation system with tactile sensing.

Combines scalable autonomous reward modeling with RL-based refinement to improve vision-language-action policies on long-horizon dexterous manipulation tasks via autonomous rollouts. Demonstrates significant gains over imitation learning baselines.

Introduces coordination-aware structured action modeling for dual-arm robotic systems within a VLA framework. Addresses the unique challenges of bimanual manipulation through specialized action representations.