5 citations · 16 across the 32 of their papers we have counts for
33 papers
PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
Chengyang He, Tanishq Duhan, Gadiel Sznaier Camps +6
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-gu…
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
Peizhuo Li, Hongyi Li, Mingfeng Fan +13
Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively…
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Yifeng Zhang, Jieming Chen, Tingguang Zhou +4
Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency i…
GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control
Shuhao Liao, Peizhuo Li, Xinrong Yang +7
Training reinforcement learning (RL) policies for legged robots remains challenging due to high-dimensional continuous actions, hardware constraints, and limited exploration. Exist…
FARE: Fast-Slow Agentic Robotic Exploration
Shuhao Liao, Xuxin Lv, Jeric Lew +6
This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration fram…
ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation
Shizhe Zhang, Jingsong Liang, Zhitao Zhou +6
Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps,…
AID: Agent Intent from Diffusion for Multi-Agent Informative Path Planning
Jeric Lew, Yuhong Cao, Derek Ming Siang Tan +1
Information gathering in large-scale or time-critical scenarios (e.g., environmental monitoring, search and rescue) requires broad coverage within limited time budgets, motivating…
APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots
Shivam Sood, Laukik Nakhwa, Sun Ge +7
Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. Despite the recent advancements in motion tracking, most existing method…
HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward
Yuhong Cao, Yizhuo Wang, Jingsong Liang +4
This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. We present HEADER, an attentio…
GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration
Haozhan Ni, Jingsong Liang, Chenyu He +2
Autonomous robot exploration (ARE) is the process of a robot autonomously navigating and mapping an unknown environment. Recent Reinforcement Learning (RL)-based approaches typical…
HeLoM: Hierarchical Learning for Whole-Body Loco-Manipulation by a Hexapod Robot
Xinrong Yang, Peizhuo Li, Hongyi Li +8
In nature, animals often need to move/manipulate objects comparable in weight/size to their own bodies. Compared to grasping and carrying, pushing provides a more straightforward a…
KiVi: Kinesthetic-Visuospatial Integration for Dynamic and Safe Egocentric Legged Locomotion
Peizhuo Li, Hongyi Li, Yuxuan Ma +8
Vision-based locomotion has shown great promise in enabling legged robots to perceive and adapt to complex environments. However, visual information is inherently fragile, being vu…
Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
Pu Feng, Size Wang, Yuhong Cao +3
The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance…
CogniPlan: Uncertainty-Guided Path Planning with Conditional Generative Layout Prediction
Yizhuo Wang, Haodong He, Jingsong Liang +3
Path planning in unknown environments is a crucial yet inherently challenging capability for mobile robots, which primarily encompasses two coupled tasks: autonomous exploration an…
Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao +2
In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot…
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning
Jiaqi Cheng, Mingfeng Fan, Xuefeng Zhang +4
Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring. These tasks often involve se…
Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild
Derek Ming Siang Tan, Shailesh, Boyang Liu +8
To perform outdoor visual navigation and search, a robot may leverage satellite imagery to generate visual priors. This can help inform high-level search strategies, even when such…
APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots
Shivam Sood, Laukik Nakhwa, Sun Ge +7
Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. While motion tracking can reproduce reference gaits, many approaches sti…
HELM: Human-Preferred Exploration with Language Models
Shuhao Liao, Xuxin Lv, Yuhong Cao +3
In autonomous exploration tasks, robots are required to explore and map unknown environments while efficiently planning in dynamic and uncertain conditions. Given the significant v…
MARVEL: Multi-Agent Reinforcement Learning for constrained field-of-View multi-robot Exploration in Large-scale environments
Jimmy Chiun, Shizhe Zhang, Yizhuo Wang +2
In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like L…
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Peizhuo Li, Hongyi Li, Ge Sun +7
Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use p…
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
Shuhao Liao, Weihang Xia, Yuhong Cao +4
The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is t…
DARE: Diffusion Policy for Autonomous Robot Exploration
Yuhong Cao, Jeric Lew, Jingsong Liang +2
Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the curren…
IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
Derek Ming Siang Tan, Yixiao Ma, Jingsong Liang +3
Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be…