43 citations · 105 across the 17 of their papers we have counts for
19 papers
Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping
Teng Guo, Baichuan Huang, Jingjin Yu
Accurate 6D object pose estimation is a prerequisite for successfully completing robotic prehensile and non-prehensile manipulation tasks. At present, 6D pose estimation for roboti…
Efficiently Manipulating Clutter via Learning and Search-Based Reasoning
Baichuan Huang
This thesis presents novel algorithms to advance robotic object rearrangement, a critical task for autonomous systems in applications like warehouse automation and household assist…
Toward Holistic Planning and Control Optimization for Dual-Arm Rearrangement
Kai Gao, Zihe Ye, Duo Zhang +2
Long-horizon task and motion planning (TAMP) is notoriously difficult to solve, let alone optimally, due to the tight coupling between the interleaved (discrete) task and (continuo…
EARL: Eye-on-Hand Reinforcement Learner for Dynamic Grasping with Active Pose Estimation
Baichuan Huang, Jingjin Yu, Siddarth Jain
In this paper, we explore the dynamic grasping of moving objects through active pose tracking and reinforcement learning for hand-eye coordination systems. Most existing vision-bas…
Toward Optimal Tabletop Rearrangement with Multiple Manipulation Primitives
Baichuan Huang, Xujia Zhang, Jingjin Yu
In practice, many types of manipulation actions (e.g., pick-n-place and push) are needed to accomplish real-world manipulation tasks. Yet, limited research exists that explores the…
LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
Haonan Chang, Kai Gao, Kowndinya Boyalakuntla +5
We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within…
Learning Generalizable Pivoting Skills
Xiang Zhang, Siddarth Jain, Baichuan Huang +2
The skill of pivoting an object with a robotic system is challenging for the external forces that act on the system, mainly given by contact interaction. The complexity increases w…
Minimizing Running Buffers for Tabletop Object Rearrangement: Complexity, Fast Algorithms, and Applications
Kai Gao, Si Wei Feng, Baichuan Huang +1
For rearranging objects on tabletops with overhand grasps, temporarily relocating objects to some buffer space may be necessary. This raises the natural question of how many simult…
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning
Baichuan Huang, Abdeslam Boularias, Jingjin Yu
We propose a novel Parallel Monte Carlo tree search with Batched Simulations (PMBS) algorithm for accelerating long-horizon, episodic robotic planning tasks. Monte Carlo tree searc…
Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter
Baichuan Huang, Teng Guo, Abdeslam Boularias +1
In this study, working with the task of object retrieval in clutter, we have developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable…
Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration
Yuhan Zhao, Baichuan Huang, Jingjin Yu +1
In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robo…
Fast High-Quality Tabletop Rearrangement in Bounded Workspace
Kai Gao, Darren Lau, Baichuan Huang +2
In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a c…
Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement
Baichuan Huang, Shuai D. Han, Jingjin Yu +1
This paper considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions. Object retrieval in dense clu…
DIPN: Deep Interaction Prediction Network with Application to Clutter Removal
Baichuan Huang, Shuai D. Han, Abdeslam Boularias +1
We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex interactions that ensue as a robot end-effector pushes multiple objects, whose physical prop…
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Baichuan Huang, Hongwei Yi, Can Huang +3
The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth…
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Baichuan Huang, Hongwei Yi, Can Huang +3
The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth…
Advanced Autonomy on a Low-Cost Educational Drone Platform
Luke Eller, Theo Guerin, Baichuan Huang +4
PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignm…
A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
Baichuan Huang, Jun Zhao, Jingbin Liu
Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM inc…
Planning with State Abstractions for Non-Markovian Task Specifications
Yoonseon Oh, Roma Patel, Thao Nguyen +3
Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction. The example command ``go to the kitchen before going to the se…