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Found 20 papers, 2 papers with code
LaPlaSS: Latent Space Planning for Stochastic Systems
We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics and is an order of magnitude more efficient than the state of the art.
Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans
When agents collaborate on a task, it is important that they have some shared mental model of the task routines -- the set of feasible plans towards achieving the goals.
P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving
Motion prediction is crucial in enabling safe motion planning for autonomous vehicles in interactive scenarios.
Hierarchical Constrained Stochastic Shortest Path Planning via Cost Budget Allocation
Stochastic sequential decision making often requires hierarchical structure in the problem where each high-level action should be further planned with primitive states and actions.
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
Predicting future motions of road participants is an important task for driving autonomously in urban scenes.
TIP: Task-Informed Motion Prediction for Intelligent Vehicles
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts.
Generalized Conflict-directed Search for Optimal Ordering Problems
In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning.
Moment-Based Exact Uncertainty Propagation Through Nonlinear Stochastic Autonomous Systems
Moments of uncertain states can be used in estimation, planning, control, and safety analysis of stochastic dynamical systems.
Fast-reactive probabilistic motion planning for high-dimensional robots
Many real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots.
Helpfulness as a Key Metric of Human-Robot Collaboration
As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions.
CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems.
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems.
Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments
In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles.
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors.
RADMPC: A Fast Decentralized Approach for Chance-Constrained Multi-Vehicle Path-Planning
This approach is intractable as fleet size increases because computation time is exponential with respect to the number of vehicles being planned over due to a polynomial increase in coupling constraints between vehicle pairs.
Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with Risk Bounding Functions
Chance Constrained Markov Decision Processes maximize reward subject to a bounded probability of failure, and have been frequently applied for planning with potentially dangerous outcomes or unknown environments.
Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk
The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning.
