Search Results for author:
Found 21 papers, 3 papers with code
Iterated Piecewise Affine (IPA) Approximation for Language Modeling
In this work, we demonstrate the application of a first-order Taylor expansion to approximate a generic function $F: R^{n \times m} \to R^{n \times m}$ and utilize it in language modeling.
Convex Risk Bounded Continuous-Time Trajectory Planning and Tube Design in Uncertain Nonconvex Environments
To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM
Third, we apply a Siamese architecture on BLOOM model with a contrastive objective to ease the multi-lingual labeled data scarcity.
Real-Time Tube-Based Non-Gaussian Risk Bounded Motion Planning for Stochastic Nonlinear Systems in Uncertain Environments via Motion Primitives
We consider the motion planning problem for stochastic nonlinear systems in uncertain environments.
Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems
In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
Karyotype AI for Precision Oncology
These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs).
Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering
When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report.
Risk Conditioned Neural Motion Planning
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
Automatic Curricula via Expert Demonstrations
We propose Automatic Curricula via Expert Demonstrations (ACED), a reinforcement learning (RL) approach that combines the ideas of imitation learning and curriculum learning in order to solve challenging robotic manipulation tasks with sparse reward functions.
Optimal Mixed Discrete-Continuous Planningfor Linear Hybrid Systems
Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-vehicle transportation systems.
Continuous Control
Robotics
Systems and Control
Systems and Control
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes
We introduce the DAG-GP model, which linearly combines latent Gaussian processes so that MOGP outputs follow a directed acyclic graph structure.
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances.
Motion Planning
Robotics
Multiagent Systems
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals.
Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models for predictions of both agent positions and controls.
Impacts of Social Distancing Policies on Mobility and COVID-19 Case Growth in the US
Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States.
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state.
Efficiently Exploring Ordering Problems through Conflict-directed Search
In this paper, we present Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed search method to incrementally and systematically generate event total orders given ordering relations and conflicts returned by sub-solvers.
Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty
In temporal planning, many different temporal network formalisms are used to model real world situations.
Drake: An Efficient Executive for Temporal Plans with Choice
We benchmark Drakes performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices.
