4 citations · 11 across the 14 of their papers we have counts for
14 papers
OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles
Taekyung Kim, Hun Kuk Park, Renya Wada +3
Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present OcclusionCBF, a safety filter tha…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
Learned IMU Bias Prediction for Invariant Visual Inertial Odometry
Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal +2
Autonomous mobile robots operating in novel environments depend critically on accurate state estimation, often utilizing visual and inertial measurements. Recent work has shown tha…
Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions
Minnan Zhou, Mustafa Shaikh, Vatsalya Chaubey +4
This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its…
Variable-Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots
Chuong Nguyen, Abdullah Altawaitan, Thai Duong +2
Achieving both target accuracy and robustness in dynamic maneuvers with long flight phases, such as high or long jumps, has been a significant challenge for legged robots. To addre…
SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
Xu Liu, Jiuzhou Lei, Ankit Prabhu +5
This paper develops a real-time decentralized metric-semantic SLAM algorithm that enables a heterogeneous robot team to collaboratively construct object-based metric-semantic maps.…
Riemannian Optimization for Active Mapping with Robot Teams
Arash Asgharivaskasi, Fritz Girke, Nikolay Atanasov
Autonomous exploration of unknown environments using a team of mobile robots demands distributed perception and planning strategies to enable efficient and scalable performance. Id…
Multi-Robot Object SLAM Using Distributed Variational Inference
Hanwen Cao, Sriram Shreedharan, Nikolay Atanasov
Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by cent…
Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping
Arash Asgharivaskasi, Nikolay Atanasov
This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approac…
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
Thai Duong, Abdullah Altawaitan, Jason Stanley +1
Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently…
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Eduardo Sebastian, Thai Duong, Nikolay Atanasov +2
The networked nature of multi-robot systems presents challenges in the context of multi-agent reinforcement learning. Centralized control policies do not scale with increasing numb…
Optimal Scene Graph Planning with Large Language Model Guidance
Zhirui Dai, Arash Asgharivaskasi, Thai Duong +4
Recent advances in metric, semantic, and topological mapping have equipped autonomous robots with semantic concept grounding capabilities to interpret natural language tasks. This…
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal +2
Reliable autonomous navigation requires adapting the control policy of a mobile robot in response to dynamics changes in different operational conditions. Hand-designed dynamics mo…
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation
Baoqian Wang, Junfei Xie, Nikolay Atanasov
Most multi-agent reinforcement learning (MARL) methods are limited in the scale of problems they can handle. With increasing numbers of agents, the number of training iterations re…