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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
Conference Proceeding talk 3 on Relevant Topic in Your Field
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Learning to Generate 6-DoF Grasp Poses with Reachability Awareness
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Presentation at ICRA 2020.
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.