Marmot Lab
Welcome to the Multi-Agent Robotic Motion (MARMOT) Laboratory at the National University of Singapore (NUS)!
Lab Director: Prof Guillaume Sartoretti
NUS Webpage: https://www.eng.nus.edu.sg/me/staff/guillaume-a-sartoretti/
Lab Website: https://www.marmotlab.org/
[CoRL25] Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild
[RSS25] SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
[IROS24] Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
ViPER: Visibility-based Pursuit-Evasion via Reinforcement Learning - Spotlight Video (CoRL 2024)
Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies
[Favorable Scenario] - ForMIC - 64 Agents, Infinite Resources, 0% Obstacles, 3 Long Wipeouts
[Favorable Scenario] - Centralized - 64 Agents, Infinite Resources, 0% Obstacles, 3 Long Wipeouts
[Average Scenario] - Centralized - 32 Agents, Depleting Resources, 0% Obstacles, No Wipeouts
[Unfavorable Scenario] - ForMIC - 32 Agents, Depleting Resources, 5% Obstacles, No Wipeouts
[Favorable Scenario] - Cardinality-MR - 64 Agents, Infinite Resources, 0% Obstacles, 3 Long Wipeouts
[Favorable Scenario] - C-SAF-11 - 64 Agents, Infinite Resources, 0% Obstacles, 3 Long Wipeouts
[Unfavorable Scenario] - Centralized - 32 Agents, Depleting Resources, 5% Obstacles, No Wipeouts
[Average Scenario] - C-SAF-11 - 32 Agents, Depleting Resources, 0% Obstacles, No Wipeouts
[Average Scenario] - Cardinality-MR - 32 Agents, Depleting Resources, 0% Obstacles, No Wipeouts
[Unfavorable Scenario] - C-SAF-11 - 32 Agents, Depleting Resources, 5% Obstacles, No Wipeouts
[Average Scenario] - ForMIC - 32 Agents, Depleting Resources, 0% Obstacles, No Wipeouts
[Unfavorable Scenario] - Cardinality-MR - 32 Agents, Depleting Resources, 5% Obstacles, No Wipeouts
Yixuan XIA - First Fourier-Based Footfall Planning Success
[No-Gradient Pheromones Test] - ForMIC - 64 Agents, Infinite Resources, 10% Obstacles, No Wipeouts
DAN: Decentralized Attention-based Neural Network to Solve the MinMax mTSP
ForMIC: Foraging via Multiagent RL with Implicit Communication
Pushing a wheeled cart using a hind legged gait
Gait transitions between common hexapedal gaits
Alternate tripod to hind legged gait transition
Gait transition to hind legged gait to enable mobile manupulation
[Noisy Pheromones Test] - ForMIC - 16 Agents, Infinite Resources, 0% Obstacles, No Wipeouts
PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Lifelong
Decentralised Multi Agent Path Finding with Heterogeneous Speeds
Model based Dynamic Obstacle Avoidance on Inclined Surface
Obstacle Avoidance for A Hexapod Robot Based on FFT control