425 citations · 739 across the 25 of their papers we have counts for
26 papers
Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial
Max Muchen Sun, Cem Bilaloglu, Ananya Rao +6
Diffusion learning leverages the statistical mechanism of diffusion processes for learning, reasoning, and inferring complex distributions from data. Recent advances in diffusion l…
A Tilt-Rotor UAV with a Gripper for Stable Contact-Based Tasks via Environmental Anchoring
Joshua Taylor, Nursultan Imanberdiyev, Wei-Yun Yau +2
Maintaining a stable pose during physical interaction is a significant challenge for aerial robots, often limiting their use in contact-based tasks. This paper presents a novel unc…
The Omega Turn: A General Turning Template for Elongate Robots
Baxi Chong, Tianyu Wang, Kelimar Diaz +12
Elongate limbless robots have the potential to locomote through tightly packed spaces for applications such as search-and-rescue and industrial inspections. The capability to effec…
COMPASS: Cooperative Multi-Agent Persistent Monitoring using Spatio-Temporal Attention Network
Xingjian Zhang, Yizhuo Wang, Guillaume Sartoretti
Persistent monitoring of dynamic targets is essential in real-world applications such as disaster response, environmental sensing, and wildlife conservation, where mobile agents mu…
STAR: Swarm Technology for Aerial Robotics Research
Jimmy Chiun, Yan Rui Tan, Yuhong Cao +2
In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Des…
Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion
Ge Sun, Milad Shafiee, Peizhuo Li +3
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in th…
Deep Reinforcement Learning-based Large-scale Robot Exploration
Yuhong Cao, Rui Zhao, Yizhuo Wang +2
In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our D…
ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas
Chengyang He, Tianze Yang, Tanishq Duhan +2
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an ess…
DecAP: Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies
Shivam Sood, Ge Sun, Peizhuo Li +1
Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter's compliant and robust nature. In parallel to t…
SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control
Harsh Goel, Yifeng Zhang, Mehul Damani +1
Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive traffic signal control to optimize the travel time of vehicles over large urban networks. Ho…
Legged Robots for Object Manipulation: A Review
Yifeng Gong, Ge Sun, Aditya Nair +5
Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typic…
Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets
Yizhuo Wang, Yutong Wang, Yuhong Cao +1
This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensi…
Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning
Tianze Yang, Yuhong Cao, Guillaume Sartoretti
In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light i…
SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding
Yutong Wang, Bairan Xiang, Shinan Huang +1
Trading off performance guarantees in favor of scalability, the Multi-Agent Path Finding (MAPF) community has recently started to embrace Multi-Agent Reinforcement Learning (MARL),…
ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration
Yuhong Cao, Tianxiang Hou, Yizhuo Wang +2
In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during e…
Distributed Reinforcement Learning for Robot Teams: A Review
Yutong Wang, Mehul Damani, Pamela Wang +2
Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising app…
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems
Yutong Wang, Guillaume Sartoretti
Decentralized cooperation in partially-observable multi-agent systems requires effective communications among agents. To support this effort, this work focuses on the class of prob…
A general locomotion control framework for multi-legged locomotors
Baxi Chong, Yasemin O. Aydin, Jennifer M. Rieser +13
Serially connected robots are promising candidates for performing tasks in confined spaces such as search-and-rescue in large-scale disasters. Such robots are typically limbless, a…
DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem
Yuhong Cao, Zhanhong Sun, Guillaume Sartoretti
The multiple traveling salesman problem (mTSP) is a well-known NP-hard problem with numerous real-world applications. In particular, this work addresses MinMax mTSP, where the obje…
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
Florian Laurent, Manuel Schneider, Christian Scheller +24
The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and th…
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
Sharada Mohanty, Erik Nygren, Florian Laurent +11
Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operatio…
PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong
Mehul Damani, Zhiyao Luo, Emerson Wenzel +1
Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In parti…
ForMIC: Foraging via Multiagent RL with Implicit Communication
Samuel Shaw, Emerson Wenzel, Alexis Walker +1
Multi-agent foraging (MAF) involves distributing a team of agents to search an environment and extract resources from it. Nature provides several examples of highly effective forag…
Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots
Guillaume Sartoretti, William Paivine, Yunfei Shi +2
State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously upd…