We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework
including best practices for fine-tuning diffusion-based policies (eg Diffusion Policy) in …

We designed a modular robotic control stack that provides a customizable and accessible
interface to the Franka Emika Panda Research robot. This framework abstracts high-level …

We introduce CyberDemo a novel approach to robotic imitation learning that leverages
simulated human demonstrations for real-world tasks. By incorporating extensive data …

Robotic Information Gathering (RIG) is a foundational research topic that answers how a
robot (team) collects informative data to efficiently build an accurate model of an unknown …

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and
unsolved problem. A number of solutions have been proposed in recent years, but we have …

Reinforcement learning (RL) has demonstrated great success in the past several years.
However, most of the scenarios focus on simulated environments. One of the main …

Simulation parameter settings such as contact models and object geometry approximations
are critical to training robust robotic policies capable of transferring from simulation to real …

In this work, we propose a new class of learnable optimizers, called Mnemosyne. It is based
on the novel spatio-temporal low-rank implicit attention Transformers that can learn to train …

In image-guided surgery, deformation of soft tissues can cause substantial errors in targeting
internal targets, since deformation can affect the translation of preoperative image-based …

Task and motion planning (TAMP) algorithms have been developed to help robots plan
behaviors in discrete and continuous spaces. Robots face complex real-world scenarios …