252 citations · 689 across the 37 of their papers we have counts for
23 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
Protective Policy Transfer
Wenhao Yu, C. Karen Liu, Greg Turk
Being able to transfer existing skills to new situations is a key capability when training robots to operate in unpredictable real-world environments. A successful transfer algorit…
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
Zhuo Xu, Wenhao Yu, Alexander Herzog +6
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning…
Learning Human Search Behavior from Egocentric Visual Inputs
Maks Sorokin, Wenhao Yu, Sehoon Ha +1
"Looking for things" is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly…
Policy Transfer via Kinematic Domain Randomization and Adaptation
Ioannis Exarchos, Yifeng Jiang, Wenhao Yu +1
Transferring reinforcement learning policies trained in physics simulation to the real hardware remains a challenge, known as the "sim-to-real" gap. Domain randomization is a simpl…
Learning Fast Adaptation with Meta Strategy Optimization
Wenhao Yu, Jie Tan, Yunfei Bai +2
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-l…
Sim-to-Real Transfer for Biped Locomotion
Wenhao Yu, Visak CV Kumar, Greg Turk +1
We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identif…
Learning Human Behaviors for Robot-Assisted Dressing
Alexander Clegg, Wenhao Yu, Jie Tan +3
We investigate robotic assistants for dressing that can anticipate the motion of the person who is being helped. To this end, we use reinforcement learning to create models of huma…
Multi-task Learning with Gradient Guided Policy Specialization
Wenhao Yu, C. Karen Liu, Greg Turk
We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization tra…
Learning to Navigate Cloth using Haptics
Alexander Clegg, Wenhao Yu, Zackory Erickson +3
We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a…