252 citations · 689 across the 37 of their papers we have counts for
40 papers
A Unified Perspective on Multiple Shooting In Differential Dynamic Programming
He Li, Wenhao Yu, Tingnan Zhang +1
Differential Dynamic Programming (DDP) is an efficient computational tool for solving nonlinear optimal control problems. It was originally designed as a single shooting method and…
GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT
Yifan Zhang, Cheng Wei, Shangyou Wu +2
Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. For example, in the task of facility siting, the Buffer tool is usua…
Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism
J. Taery Kim, Wenhao Yu, Yash Kothari +3
This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the…
Datasets and Benchmarks for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Haohong Lin +8
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation…
Language to Rewards for Robotic Skill Synthesis
Wenhao Yu, Nimrod Gileadi, Chuyuan Fu +17
Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing.…
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
Yuxiang Yang, Guanya Shi, Xiangyun Meng +4
We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-leve…
SayTap: Language to Quadrupedal Locomotion
Yujin Tang, Wenhao Yu, Jie Tan +3
Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint an…
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
Joanne Truong, April Zitkovich, Sonia Chernova +4
We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual navigation approach, trained solely in simulated short-range indoor environments, and demonstrates zero-shot…
Pre-training Language Models for Comparative Reasoning
Mengxia Yu, Zhihan Zhang, Wenhao Yu +1
Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability. In this paper, we propose a n…
Continuous Versatile Jumping Using Learned Action Residuals
Yuxiang Yang, Xiangyun Meng, Wenhao Yu +3
Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement lea…
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Yihang Yao +4
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy fr…
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
A Survey of Deep Learning for Mathematical Reasoning
Pan Lu, Liang Qiu, Wenhao Yu +2
Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The developmen…
Versatile Real-Time Motion Synthesis via Kino-Dynamic MPC with Hybrid-Systems DDP
He Li, Tingnan Zhang, Wenhao Yu +1
Specialized motions such as jumping are often achieved on quadruped robots by solving a trajectory optimization problem once and executing the trajectory using a tracking controlle…
Zero-Shot Retargeting of Learned Quadruped Locomotion Policies Using Hybrid Kinodynamic Model Predictive Control
He Li, Tingnan Zhang, Wenhao Yu +1
Reinforcement Learning (RL) has witnessed great strides for quadruped locomotion, with continued progress in the reliable sim-to-real transfer of policies. However, it remains a ch…
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang +3
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, eff…
Enhancing Automated Software Traceability by Transfer Learning from Open-World Data
Jinfeng Lin, Amrit Poudel, Wenhao Yu +3
Software requirements traceability is a critical component of the software engineering process, enabling activities such as requirements validation, compliance verification, and sa…
Learning Semantics-Aware Locomotion Skills from Human Demonstration
Yuxiang Yang, Xiangyun Meng, Wenhao Yu +3
The semantics of the environment, such as the terrain type and property, reveals important information for legged robots to adjust their behaviors. In this work, we present a frame…
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
Alejandro Escontrela, Xue Bin Peng, Wenhao Yu +4
Training a high-dimensional simulated agent with an under-specified reward function often leads the agent to learn physically infeasible strategies that are ineffective when deploy…
Safe Reinforcement Learning for Legged Locomotion
Tsung-Yen Yang, Tingnan Zhang, Linda Luu +3
Designing control policies for legged locomotion is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools…
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Yan Du, Qiuhua Huang, Renke Huang +4
In this work we propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective…
Robot Learning from Randomized Simulations: A Review
Fabio Muratore, Fabio Ramos, Greg Turk +3
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to gen…
Learning from Counterfactual Links for Link Prediction
Tong Zhao, Gang Liu, Daheng Wang +2
Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and exist…