Search Results for author:
Found 17 papers, 5 papers with code
Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation
Large real-world robot datasets hold great potential to train generalist robot models, but scaling real-world human data collection is time-consuming and resource-intensive.
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly handle real-world variability, and rapidly learn new tasks.
DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
To this end, we introduce DexMimicGen, a large-scale automated data generation system that synthesizes trajectories from a handful of human demonstrations for humanoid robots with dexterous hands.
One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation
Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning.
Flow as the Cross-Domain Manipulation Interface
By leveraging real-world human videos and simulated robot play data, we bypass the challenges of teleoperating physical robots in the real world, resulting in a scalable system for diverse tasks.
DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects
Manipulation of elastoplastic objects like dough often involves topological changes such as splitting and merging.
XSkill: Cross Embodiment Skill Discovery
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
Towards Generalist Robots: A Promising Paradigm via Generative Simulation
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment
We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions.
UMPNet: Universal Manipulation Policy Network for Articulated Objects
We introduce the Universal Manipulation Policy Network (UMPNet) -- a single image-based policy network that infers closed-loop action sequences for manipulating arbitrary articulated objects.
AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy
We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers.
Robotics
Learning 3D Dynamic Scene Representations for Robot Manipulation
3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time.
Modeling Parts, Structure, and System Dynamics via Predictive Learning
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.
