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Found 11 papers, 6 papers with code
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Task and Motion Planning (TAMP) approaches are effective at planning long-horizon autonomous robot manipulation.
CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation
CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene.
Motion Policy Networks
Collision-free motion generation in unknown environments is a core building block for robot manipulation.
Object Rearrangement Using Learned Implicit Collision Functions
The learned model outperforms both traditional pipelines and learned ablations by 9. 8% in accuracy on a dataset of simulated collision queries and is 75x faster than the best-performing baseline.
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular.
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Grasping in cluttered environments is a fundamental but challenging robotic skill.
Self-supervised 6D Object Pose Estimation for Robot Manipulation
In this way, our system is able to continuously collect data and improve its pose estimation modules.
Robotics
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
We evaluate our approach in simulation and real-world robot experiments.
The RBO Dataset of Articulated Objects and Interactions
Each interaction with an object is annotated with the ground truth poses of its rigid parts and the kinematic state obtained by a motion capture system.
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration
In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks.
