Xibai Lou

Xibai Lou

Ph.D. Student at the University of Minnesota

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Pages

Posts

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portfolio

publications

Learning to generate 6-dof grasp poses with reachability awareness

Published in IEEE International Conference on Robotics and Automation (ICRA), 2020

A voxel-based deep 3D Convolutional Neural Network (3D CNN) that generates feasible 6-DoF grasp poses in unrestricted workspace with reachability awareness. The reachability predictor evaluates if the grasp pose is reachable by exploiting large-scale synthetic datasets.

Recommended citation: X Lou, Y Yang, C Choi, Learning to generate 6-dof grasp poses with reachability awareness, IEEE International Conference on Robotics and Automation (ICRA) 2020, 1532-1538 . https://arxiv.org/pdf/1910.06404.pdf

Attribute-Based Robotic Grasping with One-Grasp Adaptation

Published in IEEE International Conference on Robotics and Automation (ICRA), 2021

We introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances.

Recommended citation: Yang Yang, Yuanhao Liu, Hengyue Liang, Xibai Lou and Changhyun Choi, Attribute-Based Robotic Grasping with One-Grasp Adaptation, ICRA 2021. https://arxiv.org/pdf/2104.02271.pdf

Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures

Published in IEEE International Conference on Robotics and Automation (ICRA), 2021

Real-world objects may not be graspable with a single parallel gripper, but only with harnessing environment fixtures (e.g., walls, furniture, heavy objects). A slide-to-wall action is proposed as well as the Target-Oriented Deep Q-Network (TO-DQN) to efficiently learn visual affordance maps (i.e., Q-maps) to guide robot actions.

Recommended citation: Hengyue Liang, Xibai Lou, Yang Yang and Changhyun Choi, Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures, ICRA 2021. https://arxiv.org/pdf/1910.03781.pdf

Collision-Aware Target-Driven Object Grasping in Constrained Environments

Published in IEEE International Conference on Robotics and Automation (ICRA), 2021

We propose a novel Collision-Aware Reachability Predictor (CARP) for 6-DoF grasping systems that learns to estimate the collision-free probabilities from partial/noisy observations and significantly improves grasping efficiency in challenging environments.

Recommended citation: X Lou, Y Yang, C Choi, Collision-Aware Target-Driven Object Grasping in Constrained Environments, IEEE International Conference on Robotics and Automation (ICRA) 2021. https://arxiv.org/pdf/2104.00776.pdf

talks

teaching

Teaching Assitant for EE2361

Undergraduate course, University of Minnesota, Electrical and Computer Engineering, 2020

Basic computer organization, opcodes, assembly language programming, logical operations and bit manipulation in C, stack structure, timers, parallel/serial input/output, buffers, input pulse-width and period measurements, PWM output, interrupts and multi-tasking, using special-purpose features such as A/D converters. Integral lab.

Teaching Assistant for EE2301

Undergraduate Course, University of Minnesota, Electrical and Computer Engineering, 2020

Boolean algebra, logic gates, combinational logic, logic simplification, sequential logic, design of synchronous sequential logic, Verilog modeling, design of logic circuits. Integral lab.