41 citations · 56 across the 18 of their papers we have counts for
21 papers
120 Minutes and a Laptop: Minimalist Image-goal Navigation via Unsupervised Exploration and Offline RL
Xiaoming Liu, Borong Zhang, Qingbiao Li +1
The prevailing paradigm for image-goal visual navigation often assumes access to large-scale datasets, substantial pretraining, and significant computational resources. In this wor…
Learning Mixture Density via Natural Gradient Expectation Maximization
Yutao Chen, Jasmine Bayrooti, Steven Morad
Mixture density networks are neural networks that produce Gaussian mixtures to represent continuous multimodal conditional densities. Standard training procedures involve maximum l…
Investigating Memory in Model-Free RL with POPGym Arcade
Zekang Wang, Zhe He, Borong Zhang +2
How should we analyze memory in deep RL? We introduce tools for analyzing policies under partial observability and revealing how agents use memory to make decisions. To utilize the…
Language-Conditioned Offline RL for Multi-Robot Navigation
Steven Morad, Ajay Shankar, Jan Blumenkamp +1
We present a method for developing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from…
CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications
Jan Blumenkamp, Steven Morad, Jennifer Gielis +1
Autonomous robot operation in unstructured environments is often underpinned by spatial understanding through vision. Systems composed of multiple concurrently operating robots add…
Generalising Multi-Agent Cooperation through Task-Agnostic Communication
Dulhan Jayalath, Steven Morad, Amanda Prorok
Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication st…
Recurrent Reinforcement Learning with Memoroids
Steven Morad, Chris Lu, Ryan Kortvelesy +3
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov sta…
Reinforcement Learning with Fast and Forgetful Memory
Steven Morad, Ryan Kortvelesy, Stephan Liwicki +1
Nearly all real world tasks are inherently partially observable, necessitating the use of memory in Reinforcement Learning (RL). Most model-free approaches summarize the trajectory…
Generalised f-Mean Aggregation for Graph Neural Networks
Ryan Kortvelesy, Steven Morad, Amanda Prorok
Graph Neural Network (GNN) architectures are defined by their implementations of update and aggregation modules. While many works focus on new ways to parametrise the update module…
POPGym: Benchmarking Partially Observable Reinforcement Learning
Steven Morad, Ryan Kortvelesy, Matteo Bettini +2
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contem…
Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning
Ryan Kortvelesy, Steven Morad, Amanda Prorok
The problem of permutation-invariant learning over set representations is particularly relevant in the field of multi-agent systems -- a few potential applications include unsuperv…
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
Jan Blumenkamp, Steven Morad, Jennifer Gielis +2
GNNs are a paradigm-shifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as f…
Graph Convolutional Memory using Topological Priors
Steven D. Morad, Stephan Liwicki, Ryan Kortvelesy +2
Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the…
Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
Steven D. Morad, Roberto Mecca, Rudra P. K. Poudel +2
We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric featu…
Building Small-Satellites to Live Through the Kessler Effect
Steven Morad, Himangshu Kalita, Ravi teja Nallapu +1
The rapid advancement and miniaturization of spacecraft electronics, sensors, actuators, and power systems have resulted in growing proliferation of small-spacecraft. Coupled with…
Improving Visual Feature Extraction in Glacial Environments
Steven D. Morad, Jeremy Nash, Shoya Higa +3
Glacial science could benefit tremendously from autonomous robots, but previous glacial robots have had perception issues in these colorless and featureless environments, specifica…
A Spring Propelled Extreme Environment Robot for Off-World Cave Exploration
Steven Morad, Thomas Dailey, Leonard Vance +1
Pits on the Moon and Mars are intriguing geological formations that have yet to be explored. These geological formations can provide protection from harsh diurnal temperature varia…
Coordination and Control of Multiple Climbing Robots in Transport of Heavy Loads through Extreme Terrain
Himangshu Kalita, Steven Morad, Jekan Thangavelautham
The discovery of ice deposits in the permanently shadowed craters of the lunar North and South Pole Moon presents an important opportunity for In-Situ Resource Utilization. These i…
On-Orbit Smart Camera System to Observe Illuminated and Unilluminated Space Objects
Steve Morad, Ravi Teja Nallapu, Himangshu Kalita +5
The wide availability of Commercial Off-The-Shelf (COTS) electronics that can withstand Low Earth Orbit conditions has opened avenue for wide deployment of CubeSats and small-satel…
Planning and Navigation of Climbing Robots in Low-Gravity Environments
Steven Morad, Himangshu Kalita, Jekan Thangavelautham
Advances in planetary robotics have led to wheeled robots that have beamed back invaluable science data from the surface of the Moon and Mars. However, these large wheeled robots a…
Path Planning and Navigation Inside Off-World Lava Tubes and Caves
Himangshu Kalita, Steven Morad, Jekan Thangavelautham
Detailed surface images of the Moon and Mars reveal hundreds of cave-like openings. These cave-like openings are theorized to be remnants of lava-tubes and their interior maybe in…