4 citations · 5 across the 12 of their papers we have counts for
13 papers
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…
Self-supervised Wide Baseline Visual Servoing via 3D Equivariance
Jinwook Huh, Jungseok Hong, Suveer Garg +2
One of the challenging input settings for visual servoing is when the initial and goal camera views are far apart. Such settings are difficult because the wide baseline can cause d…
Learning Continuous Cost-to-Go Functions for Non-holonomic Systems
Jinwook Huh, Daniel D. Lee, Volkan Isler
This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from info…
Cost-to-Go Function Generating Networks for High Dimensional Motion Planning
Jinwook Huh, Volkan Isler, Daniel D. Lee
This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over t…
Learning to Generate Cost-to-Go Functions for Efficient Motion Planning
Jinwook Huh, Galen Xing, Ziyun Wang +2
Traditional motion planning is computationally burdensome for practical robots, involving extensive collision checking and considerable iterative propagation of cost values. We pre…
Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner
Tarik Tosun, Eric Mitchell, Ben Eisner +6
We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of hu…
Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning
Jinwook Huh, Omur Arslan, Daniel D. Lee
In this paper, we introduce a new probabilistically safe local steering primitive for sampling-based motion planning in complex high-dimensional configuration spaces. Our local ste…
Learning Implicit Sampling Distributions for Motion Planning
Clark Zhang, Jinwook Huh, Daniel D. Lee
Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficie…