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<p class="mb-5 self-intro">I'm a roboticist dedicated to robotics research and works. I am now working in <a target="_blank" href="http://arm.eecs.umich.edu/">Autonomous Robotic Manipulation Laboratory</a> at the University of Michigan. I graduated from Nation Taiwan University, Taiwan with B.S. and M.S. degree in Electrical Engineering. My research topics include but not limited to humanoid locomotion planning, general motion planning, robot arm manipulations, 3D perceptions, and system integrations. I welcome every new possibility to explore new fileds in Robotics, Control Application, AI and Machine Learning.</p>
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<h5 class="project-thum-title">Humanoid Navigation Planning using Previous Experience</h5>
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<h5 class="project-thum-title">Mobile Manipulator Motion Planning</h5>
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<h5><a class="txt-dec-none" href="http://docs.wixstatic.com/ugd/2792ae_e5298ea1f74a48168e6c0fecedd19260.pdf" target="_blank">Search Algorithm based Planning on Finding and Grasping of Occluded Target Object with a Mobile Robot Manipulator</a><a target="_blank" class="play-icon" href="http://youtu.be/8OUOmsjJe2Y"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong><br/>
M.S. Thesis, National Taiwan University, Taiwan, 2014. </p>
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<h3 class="mb-5">Journal Paper</h3>
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<h5><a class="txt-dec-none" href="https://arxiv.org/abs/1909.09233" target="_blank">Robust Humanoid Contact Planning with Learned Zero- and One-Step Capturability Prediction</a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong>, Ludovic Righetti and Dmitry Berenson<br/>
Submitted to IEEE Robotics and Automation Letters (RA-L), 2019. (To Appear) </p>
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<h3 class="mb-5">Conference Papers</h3>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=74">Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction</a><a target="_blank" class="play-icon" href="https://youtu.be/nnQwSKe-8dM"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong>, Brahayam Ponton, Ludovic Righetti and Dmitry Berenson<br/>
IEEE International Conference on Robotics and Automation (ICRA), 2019. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=69">Humanoid Navigation Planning in Large Unstructured Environments Using Traversability-Based Segmentation</a><a target="_blank" class="play-icon" href="https://youtu.be/CWSx7qQzmtw"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=63">Humanoid Navigation in Uneven Terrain using Learned Estimates of Traversability</a><a target="_blank" class="play-icon" href="https://youtu.be/Z8xkNafgfKE"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2017. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=52">Using Previous Experience for Humanoid Navigation Planning</a><a target="_blank" class="play-icon" href="https://youtu.be/9gQ_m6CNjt8"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=53">Integrated Affordance Detection and Humanoid Locomotion Planning</a><a target="_blank" class="play-icon" href="https://youtu.be/aT6ELHUI4Wg"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray">Will Pryor, <strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016. </p>
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<h5><a class="txt-dec-none" href="https://www.dropbox.com/s/7ug8dbcazd355je/planning_on_searching_occluded_target.pdf?dl=0">Planning on Searching Occluded Target Object with a Mobile Robot Manipulator</a><a target="_blank" class="play-icon" href="https://youtu.be/8OUOmsjJe2Y"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong>, Shao-Ting Wei, Shih-An Yang and Li-Chen Fu<br/>
IEEE International Conference on Robotics and Automation (ICRA), 2015. </p>
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<h5><a class="txt-dec-none" href="https://www.dropbox.com/s/1c28coai3ercn3c/grasping_unknown_objects_using_depth_gradient_feature_with_eye_in_hand_rgbd_sensor.pdf?dl=0">Grasping Unknown Objects Using Depth Gradient Feature with Eye-In-Hand RGB-D Sensor</a><a target="_blank" class="play-icon" href="https://youtu.be/hcPqMakO-RI"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong>, Shao-Ting Wei and Li-Chen Fu<br/>
IEEE International Conference on Automation Science and Engineering (CASE), 2014. </p>
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<h5><a class="txt-dec-none" href="https://www.dropbox.com/s/t8eecoglw7332k3/grasping_the_object_with_collision_avoidance_of_wheeled_mobile_manipulator_in_dynamic_environments.pdf?dl=0">Grasping the object with collision avoidance of wheeled mobile manipulator in dynamic environments</a></h5>
<p class="gray">Pei-Wen Wu, <strong>Yu-Chi Lin</strong>, Chia-Ming Wang, and Li-Chen Fu<br/>
IEEE International Conference on Intelligent Robots and Systems (IROS), 2013. </p>
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<h2 class="text-uppercase">Humanoid Navigation Planning using Previous Experience</h2>
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We propose a humanoid robot navigation planning framework that reuses previous experience to decrease planning time. The framework is intended for navigating complex unstructured environments using both palm and foot contacts. In a complex environment, discrete-search-based contact space planners trade-off between high branching factor and action flexibility. Although approaches such as weighted A*, ARA* and ANA* could speed up the search by compromising on optimality, they can be very slow when the heuristic is inaccurate. In the proposed framework, an experience-retrieval module is added in parallel to ANA*. This module collects previously-generated motion plans and clusters them based on contact pose similarity to form a motion plan library. To retrieve an appropriate plan from the library for a given environment, the framework uses a distance between the contact poses in the plan and environment surfaces. Candidate plans are then modified with local trajectory optimization until a plan fitting the query environment is found. Our experiments show that the proposed framework outperforms planning-from-scratch in success rate in unstructured environments by at least 28% and can navigate difficult environments such as rubble and narrow corridors.
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=69">Humanoid Navigation Planning in Large Unstructured Environments Using Traversability-Based Segmentation</a><a target="_blank" class="play-icon" href="https://youtu.be/CWSx7qQzmtw"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=52">Using Previous Experience for Humanoid Navigation Planning</a><a target="_blank" class="play-icon" href="https://youtu.be/9gQ_m6CNjt8"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016. </p>
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<h2 class="text-uppercase">Learning Humanoid Traversability</h2>
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We explore discrete search-based contact space planning for humanoids using both palm and foot contact in complex unstructured environments. With a high branching factor and sparse contactable regions, it is challenging for the planner to find a contact sequence in such environments quickly. Therefore, we propose to learn a function which predicts traversability---a measure of how quickly the contact space planner can generate contact sequences to traverse a certain region. By including a learned traversability estimate into the heuristic function of the contact space planner, we can bias the planner to search the areas with more contactable regions, and thus find contact sequences more efficiently. In this paper we propose and evaluate two kinds of feature vectors for estimating traversability: Exact Contact Checking (ECC) and Approximate Contact Checking (ACC), which make different trade-offs between speed and accuracy.
The experimental results show that the proposed approach using ACC outperforms both ECC and the baseline heuristic for contact space planning; ACC increases the planning success rate by 19% and reduces average planning time by 24% compared to the baseline in difficult environments with uneven terrain.
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=69">Humanoid Navigation Planning in Large Unstructured Environments Using Traversability-Based Segmentation</a><a target="_blank" class="play-icon" href="https://youtu.be/CWSx7qQzmtw"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. </p>
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<h5><a class="txt-dec-none" href="http://arm.eecs.umich.edu/download.php?p=63">Humanoid Navigation in Uneven Terrain using Learned Estimates of Traversability</a><a target="_blank" class="play-icon" href="https://youtu.be/Z8xkNafgfKE"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong> and Dmitry Berenson<br/>
IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2017. </p>
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<h2 class="text-uppercase">Mobile Manipulator Motion Planning</h2>
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We proposed an object search planner based on A* search algorithm with tree node sampling. The proposed approach combines visual and arm manipulation search. In other words, the robot searches occluded target object by either repositioning one of the accessible object with its arm or moving its platform to view the environment from a different pose. We evaluate the proposed approach with experiment performed by real robot in the scenario which objects may occlude or block access to one another.
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In addition, we also proposed an algorithm for a mobile manipulator to reach target and avoid collision simultaneously. The algorithm utilizes potential fields to guide the local motion of the manipulator, detects possible cases when a local minimum is encountered, and escape local minimum with predefined strategies corresponding to each case.
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<h4 class="mb-5">Related Publication</h4>
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<h5><a class="txt-dec-none" href="https://www.dropbox.com/s/7ug8dbcazd355je/planning_on_searching_occluded_target.pdf?dl=0">Planning on Searching Occluded Target Object with a Mobile Robot Manipulator</a><a target="_blank" class="play-icon" href="https://youtu.be/8OUOmsjJe2Y"> <i class="fa fa-youtube-play" aria-hidden="true"></i></a></h5>
<p class="gray"><strong>Yu-Chi Lin</strong>, Shao-Ting Wei, Shih-An Yang and Li-Chen Fu<br/>
IEEE International Conference on Robotics and Automation (ICRA), 2015. </p>
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<h5><a class="txt-dec-none" href="https://www.dropbox.com/s/t8eecoglw7332k3/grasping_the_object_with_collision_avoidance_of_wheeled_mobile_manipulator_in_dynamic_environments.pdf?dl=0">Grasping the object with collision avoidance of wheeled mobile manipulator in dynamic environments</a></h5>
<p class="gray">Pei-Wen Wu, <strong>Yu-Chi Lin</strong>, Chia-Ming Wang, and Li-Chen Fu<br/>
IEEE International Conference on Intelligent Robots and Systems (IROS), 2013. </p>
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