Latest

• DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning
• RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics
• M2T2: Multi-task Masked Transformer for Object-centric Pick-and-Place
• CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
• Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
• MegaPose: 6D Pose Estimationof Novel Objects via Render & Compare
• IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
• Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
• RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
• STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation
• NeRP: Neural Rearrangement Planning for Unknown Objects
• ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
• Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
• Object Rearrangement Using Learned Implicit Collision Functions
• Reactive Human-to-Robot Handovers of Arbitrary Objects
• RGB-D Local Implicit Function for Depth Completion of Transparent Objects
• Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections
• Interpreting and Predicting Tactile Signals for the SynTouch BioTac
• Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
• Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework
• 6-DOF Grasping for Target-driven Object Manipulation in Clutter
• LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
• Self-supervised 6D Object Pose Estimation for Robot Manipulation
• 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
• A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
• The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
• PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking