Learns dexterous manipulation policies that explicitly ground actions in generative contact predictions from visuotactile observations, improving robustness on contact-rich tasks.

Introduces multi-axis preference supervision to learn dense, language-conditioned rewards across speed/precision/subtask axes without segmentation; enables compositional generalization and better long-horizon credit assignment than single-reward baselines.

Proposes a general world foundation model leveraging Next-State-Prediction to jointly generate text, images, and embodied actions within a unified framework.

A unified 2B–8B parameter model for robot navigation tasks (VLN, ObjectNav, tracking, autonomous driving) via a configurable observation protocol, with demonstrated zero-shot deployment on real quadruped robots using agentic planners.

Replaces noisy 6-DoF hand poses with relative wrist translation as a shared action space between humans and bimanual robots, enabling scalable skill acquisition from inexpensive video data. This approach outperforms full-pose baselines for robot skill learning.

Large-scale open-source framework for real-world robotic manipulation using behavior cloning, including the ABC-130K dataset with 3,500 hours and 130K+ episodes across 195 tasks. Provides hardware setups, simulators, and training recipes for Diffusion Transformers and Vision-Language-Action models.

Presents alignment techniques to scale robotic manipulation foundation models, building on the Qwen model family for dexterous robot control.

Introduces the first automated system that continuously discovers, evolves, and accumulates reusable sensorimotor skills via evolutionary search over control programs, enabling compounding multi-task, sim-to-real, and cross-embodiment transfer without retraining end-to-end policies.

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 handle dynamic scenes and long-horizon memory. Outperforms image-only VLA baselines on the BEHAVIOR-1K benchmark for mobile manipulation.

A single-camera sim-to-real framework that uses physically consistent 3D Gaussian Splatting augmentations to achieve zero-shot transfer of dexterous in-hand reorientation policies to an Allegro hand. The approach trains entirely on consumer hardware while maintaining high fidelity to real-world dynamics.