20 citations · 46 across the 12 of their papers we have counts for
13 papers
Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs
Muhammad Fadhil Ginting, Sung-Kyun Kim, Oriana Peltzer +4
To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and…
Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments
Amanda Bouman, Joshua Ott, Sung-Kyun Kim +5
We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked w…
Fast and Scalable Signal Inference for Active Robotic Source Seeking
Christopher E. Denniston, Oriana Peltzer, Joshua Ott +6
In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking…
Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
Joshua Ott, Sung-Kyun Kim, Amanda Bouman +6
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, loc…
ACHORD: Communication-Aware Multi-Robot Coordination with Intermittent Connectivity
Maira Saboia, Lillian Clark, Vivek Thangavelu +16
Communication is an important capability for multi-robot exploration because (1) inter-robot communication (comms) improves coverage efficiency and (2) robot-to-base comms improves…
FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget
Oriana Peltzer, Amanda Bouman, Sung-Kyun Kim +8
We present a method for autonomous exploration of large-scale unknown environments under mission time constraints. We start by proposing the Frontloaded Information Gain Orienteeri…
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Ali Agha, Kyohei Otsu, Benjamin Morrell +69
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
Sung-Kyun Kim, Amanda Bouman, Gautam Salhotra +4
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion…
Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion
Amanda Bouman, Muhammad Fadhil Ginting, Nikhilesh Alatur +8
This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, pa…
Task-Informed Fidelity Management for Speeding Up Robotics Simulation
Abhijeet Tallavajhula, Adrian Schoisengeier, Sung-Kyun Kim +3
Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in bet…
Planning, Learning and Reasoning Framework for Robot Truck Unloading
Fahad Islam, Anirudh Vemula, Sung-Kyun Kim +3
We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for…
Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version
Sung-Kyun Kim, Rohan Thakker, Ali-akbar Agha-mohammadi
Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Pr…
SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version
Ali-akbar Agha-mohammadi, Saurav Agarwal, Sung-Kyun Kim +2
Simultaneous localization and Planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous POMDP (p…