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TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
Momchil S. Tomov, Sang Uk Lee, Hansford Hendrago +14
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art perform…
VFAS-Grasp: Closed Loop Grasping with Visual Feedback and Adaptive Sampling
Pedro Piacenza, Jiacheng Yuan, Jinwook Huh +1
We consider the problem of closed-loop robotic grasping and present a novel planner which uses Visual Feedback and an uncertainty-aware Adaptive Sampling strategy (VFAS) to close t…
HIO-SDF: Hierarchical Incremental Online Signed Distance Fields
Vasileios Vasilopoulos, Suveer Garg, Jinwook Huh +2
A good representation of a large, complex mobile robot workspace must be space-efficient yet capable of encoding relevant geometric details. When exploring unknown environments, it…
Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction
Shubham Agrawal, Nikhil Chavan-Dafle, Isaac Kasahara +3
Robotic manipulation systems operating in complex environments rely on perception systems that provide information about the geometry (pose and 3D shape) of the objects in the scen…
RAMP: Hierarchical Reactive Motion Planning for Manipulation Tasks Using Implicit Signed Distance Functions
Vasileios Vasilopoulos, Suveer Garg, Pedro Piacenza +2
We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of sampling-based and reactive approaches for motion planning. In essence, RAMP is a hierarchic…
Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordance
Zhanpeng He, Nikhil Chavan-Dafle, Jinwook Huh +2
The choice of a grasp plays a critical role in the success of downstream manipulation tasks. Consider a task of placing an object in a cluttered scene; the majority of possible gra…