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Found 9 papers, 1 papers with code
Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network
A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies.
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task.
Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines
We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms.
User Based Design and Evaluation Pipeline for Indoor Airships
Designing a controllable airship for non-expert users or preemptively evaluating the performance of desired airships has always been a very challenging problem.
Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications
Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters.
Robotics
Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems.
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance
In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments.
Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach
We propose using a linear approximate Q-learning method in which human knowledge contributes to selecting useful yet simple features of tissue manipulation while the algorithm learns to take optimal actions and accomplish the task.
Event-Triggered Diffusion Kalman Filters
Distributed state estimation strongly depends on collaborative signal processing, which often requires excessive communication and computation to be executed on resource-constrained sensor nodes.
Systems and Control Robotics Signal Processing
