21 datasets found

The dataset is used for motion planning for parabolic equations using flatness and finite-difference approximations.

The NuPlan dataset is a closed-loop motion planning benchmark for autonomous vehicles.

Classical motion planning methods and learning from hallucination approaches.

The dataset used in this paper is a set of vehicle models and obstacle avoidance formulations.

A learned collision checking model that dramatically increases collision checking speeds between point clouds for motion planning in real-world rearrangement tasks.

The proposed method uses raw depth images obtained from a front-facing camera and directly generates local motion plans in the form of smooth motion primitives that move a...

Firm is a framework for motion planning under uncertainty that uses feedback controllers to achieve robustness.

ADVT is an anytime online solver for POMDPs that scales well to POMDPs with high-dimensional continuous action spaces.

The dataset used for the experiments in the paper is a collection of robot configurations for the Pholus robot.

The dataset used for the experiments in the paper is a collection of constraint-satisfying robot configurations.

The dataset used in the paper is a collection of demonstrated trajectories for a robotic system.

A dataset of 100 workspaces for training and 10 workspaces for testing, with 200 unseen start and goal configurations in each workspace.

A dataset of 110 different workspaces for motion planning, including simple 2D (s2D), complex 2D (c2D), complex 3D (c3D), and rigid-body (rigid) environments.

A structured VAE for generative Bayesian inverse games. The dataset is used to train the VAE and to evaluate the performance of the proposed approach.

A structured VAE for generative Bayesian inverse games. The dataset is used to train the VAE and to evaluate the performance of the proposed approach.

The dataset used in the paper is a set of images and videos of objects moving in different environments, used for training and testing the Motion Planning Diffusion model.

The EDMP dataset is not explicitly mentioned in the paper, but it is used as a benchmark for the proposed EDMP algorithm.

The MπNets dataset consists of 6.54 million collision-free trajectories generated on various scenes (such as tabletop, dresser, and cubby) using two different classical planning...

The dataset is used to train the ClearanceNet model. It contains 106 training and 104 evaluation samples for each environment.

ClearanceNet is a neural network that estimates minimum separation distance, conditioned on joint robot-workspace pose. ClearanceNet is trained using large-scale hyperparameter...