A benchmark for evaluating observation space shift challenges in long-horizon robotic tasks.

We integrate perception and task planning under belief-space planning to enable strategic information gathering in open-world environments, where vision-language foundation models are used to estimate the state and its uncertainty.

We develop an approach for efficient open-vocabulary language-conditioned manipulation policy learning.

We enable a robot to rapidly and autonomously specialize parameterized skills by planning to practice them. The robot decides what skills to practice and how to practice them. The robot is left alone for hours, repeatedly practicing and improving.

We have developed ThinkGrasp, a plug-and-play vision-language grasping system for heavy clutter environment grasping strategies.

We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.

This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.

We have developed ThinkGrasp, a plug-and-play vision-language grasping system for heavy clutter environment grasping strategies.

We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.

We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.

We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.

We study sample-efficient modeling of invariant drag coefficients with equivariant networks.

We train an equivariant network for pose prediction from single 2D image by using induced and restricted representations.

We study whether Euclidean symmetry can help in reinforcement learning and planning, which models the geometric transformations between reference frames of robots.

We formulate compositional generalization in object-oriented world modeling, and propose a soft and efficient mechanism for practice.

We learn latent representations enforced with known transformation laws (group action), and apply this idea on equivariant latent world modeling.

We propose a parameterized action reinforcement learning algorithm to improve the performance of match plan generation in Bing search.

We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space.

This work aims to improve the zero-shot generalization performance in map-based navigation on novel layouts.

This work proposes a reinforcement learning framework for compositional object-oriented environments.