publications

2024

1. 

   Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains

   Shangqun Yu, Nisal Perera, Daniel Marew, and 1 more author

   In 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), 2024

   HTML Video

2. 

   A Biomechanics-Inspired Approach to Soccer Kicking for Humanoid Robots

   Daniel Marew, Nisal Perera, Shangqun Yu, and 2 more authors

   In 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), 2024

   HTML Video

3. 

   StaccaToe: A Single-Leg Robot that Mimics the Human Leg and Toe

   Nisal Perera, Shangqun Yu, Daniel Marew, and 6 more authors

   In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024

   HTML Video

4. 

   Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot

   Neil Guan, Shangqun Yu, Shifan Zhu, and 1 more author

   In 2024 21st International Conference on Ubiquitous Robots (UR), 2024

2023

1. 

   Integration of Riemannian Motion Policy with Whole-Body Control for Collision-Free Legged Locomotion

   Daniel Marew, Misha Lvovsky, Shangqun Yu, and 2 more authors

   In 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), 2023

2. 

   Dynamic Object Avoidance using Event-Data for a Quadruped Robot

   Shifan Zhu, Nisal Perera, Shangqun Yu, and 2 more authors

   IROS IPPC Workshop, 2023

   Abs Bib HTML Video

   As robots increase in agility and encounter fast- moving objects, dynamic object detection and avoidance become notably challenging. Traditional RGB cameras, burdened by motion blur and high latency, often act as the bottleneck. Event cameras have recently emerged as a promising solution for the challenges related to rapid movement. In this paper, we introduce a dynamic object avoidance framework that integrates both event and RGBD cameras. Specifically, this framework first estimates and compensates for the event’s motion to detect dynamic objects. Subsequently, depth data is combined to derive a 3D trajectory. When initiating from a static state, the robot adjusts its height based on the predicted collision point to avoid the dynamic obstacle. Through real-world experiments with the Mini-Cheetah, our approach successfully circumvents dynamic objects at speeds up to 5 m/s, achieving an 83% success rate. Supplemental video:

   @article{zhu2023dynamic,
     title = {Dynamic Object Avoidance using Event-Data for a Quadruped Robot},
     author = {Zhu, Shifan and Perera, Nisal and Yu, Shangqun and Hwang, Hochul and Kim, Donghyun},
     journal = {IROS IPPC Workshop},
     year = {2023},
   }

3. 

   Meta-learning Parameterized Skills

   Haotian Fu, Shangqun Yu, Saket Tiwari, and 2 more authors

   In Proceedings of the 40th International Conference on Machine Learning, 23–29 jul 2023

   Abs PDF

   We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks. We propose to leverage off-policy Meta-RL combined with a trajectory-centric smoothness term to learn a set of parameterized skills. Our agent can use these learned skills to construct a three-level hierarchical framework that models a Temporally-extended Parameterized Action Markov Decision Process. We empirically demonstrate that the proposed algorithms enable an agent to solve a set of highly difficult long-horizon (obstacle-course and robot manipulation) tasks.

4. AAAI

   

   Q-functionals for Value-Based Continuous Control

   Samuel Lobel, Sreehari Rammohan, Bowen He, and 2 more authors

   Proceedings of the AAAI Conference on Artificial Intelligence, Jun 2023
5. Neural Networks

   

   A domain-agnostic approach for characterization of lifelong learning systems

   Megan M. Baker, Alexander New, Mario Aguilar-Simon, and 44 more authors

   Neural Networks, Jun 2023

   Abs

   Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of “Lifelong Learning” systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development — both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

2022

1. NeurIPS

   

   Model-based Lifelong Reinforcement Learning with Bayesian Exploration

   Haotian Fu, Shangqun Yu, Michael Littman, and 1 more author

   In Advances in Neural Information Processing Systems, Jun 2022
2. RLDM

   

   Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study

   Shangqun Yu, Sreehari Rammohan, Kaiyu Zheng, and 1 more author

   In Multidisciplinary Conference on Reinforcement Learning and Decision Making, Jun 2022