474 citations · 1.1k across the 33 of their papers we have counts for
33 papers
IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation
Kaixian Qu, Jie Tan, Tingnan Zhang +3
Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation proble…
Learning manipulation of steep granular slopes for fast Mini Rover turning
Deniz Kerimoglu, Daniel Soto, Malone Lincoln Hemsley +4
Future planetary exploration missions will require reaching challenging regions such as craters and steep slopes. Such regions are ubiquitous and present science-rich targets poten…
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…
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…
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…
Mnemosyne: Learning to Train Transformers with Transformers
Deepali Jain, Krzysztof Marcin Choromanski, Avinava Dubey +4
In this work, we propose a new class of learnable optimizers, called \textit{Mnemosyne}. It is based on the novel spatio-temporal low-rank implicit attention Transformers that can…
Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization
Thomas Lew, Sumeet Singh, Mario Prats +11
We propose a framework to enable multipurpose assistive mobile robots to autonomously wipe tables to clean spills and crumbs. This problem is challenging, as it requires planning w…
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…
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski +14
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied publi…
Gesture2Path: Imitation Learning for Gesture-aware Navigation
Catie Cuan, Edward Lee, Emre Fisher +5
As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on…
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
Xuesu Xiao, Zifan Xu, Zizhao Wang +14
The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim…
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…
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…
Fast and Efficient Locomotion via Learned Gait Transitions
Yuxiang Yang, Tingnan Zhang, Erwin Coumans +2
We focus on the problem of developing energy efficient controllers for quadrupedal robots. Animals can actively switch gaits at different speeds to lower their energy consumption.…
On the Sample Complexity of Stability Constrained Imitation Learning
Stephen Tu, Alexander Robey, Tingnan Zhang +1
We study the following question in the context of imitation learning for continuous control: how are the underlying stability properties of an expert policy reflected in the sample…
SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning
Yifeng Jiang, Tingnan Zhang, Daniel Ho +4
As learning-based approaches progress towards automating robot controllers design, transferring learned policies to new domains with different dynamics (e.g. sim-to-real transfer)…
Learning Agile Robotic Locomotion Skills by Imitating Animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang +3
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many com…