Robotics and Biology Laboratory
Xing Li
Research Interests
Hi, my name is Xing Li. I am a PhD student at the Technische Universität Berlin under the supervision of Professor Oliver Brock. I am associated with the Excellence Research Cluster Science of Intelligence at Berlin.
My reserach interest is to develop a novel system that allows non-experts to intuitively teach robots complex skills through demonstrations. Different to other reserach works focus on Learning from demonstration, I am focusing on how to endow the robot with the ability to efficiently explore the environment under the guidance of human demonstrations. I believe this is a possible solution to bridge the gap in perception repertoire between humans and robots is to leverage the potential provided by the robots’ embodiment. I hope my research could contribute to the robotics community in the context of bringing robots to everyday household environments and making them accessible to everyone.
Publications
2022
Li,
Xing;
Brock,
Oliver
Learning From Demonstration Based on Environmental Constraints
IEEE Robotics and Automation Letters, 7 (4) :10938-10945
2022
Learning From Demonstration Based on Environmental Constraints
IEEE Robotics and Automation Letters, 7 (4) :10938-10945
2022
2020
Rosbach,
Sascha;
James,
Vinit;
Großjohann,
Simon;
Homoceanu,
Silviu;
Li,
Xing;
Roth,
Stefan
Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving
2020 IEEE International Conference on Robotics and Automation (ICRA), Page 6419-6425
2020
Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving
2020 IEEE International Conference on Robotics and Automation (ICRA), Page 6419-6425
2020
Rosbach,
Sascha;
Li,
Xing;
Großjohann,
Simon;
Homoceanu,
Silviu;
Roth,
Stefan
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Page 5187-5193
2020
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Page 5187-5193
2020