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Found 27 papers, 14 papers with code
Collision Avoidance Verification of Multiagent Systems with Learned Policies
We demonstrate the proposed algorithm can verify collision-free properties of a MA-NFL with agents trained to imitate a collision avoidance algorithm (Reciprocal Velocity Obstacles).
Robust Survival Analysis with Adversarial Regularization
Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has important applications in many fields, including medicine, defense, finance, and aerospace.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
DRIP: Domain Refinement Iteration with Polytopes for Backward Reachability Analysis of Neural Feedback Loops
To address this issue, we introduce DRIP, an algorithm with a refinement loop on the relaxation domain, which substantially tightens the BP set bounds.
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops
We introduce a hybrid partitioning method that uses both target set partitioning (TSP) and backreachable set partitioning (BRSP) to overcome a lower bound on estimation error that is present when using BRSP.
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems
As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe.
Backward Reachability Analysis for Neural Feedback Loops
The increasing prevalence of neural networks (NNs) in safety-critical applications calls for methods to certify their behavior and guarantee safety.
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map
Motion planning in off-road environments requires reasoning about both the geometry and semantics of the scene (e. g., a robot may be able to drive through soft bushes but not a fallen log).
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
An effective approach that has recently emerged for addressing this non-stationarity is for each agent to anticipate the learning of other agents and influence the evolution of future policies towards desirable behavior for its own benefit.
Neural Network Verification in Control
Learning-based methods could provide solutions to many of the long-standing challenges in control.
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
Our approach opens the possibility of zero-shot transfer from a single demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a domain with bounded model errors/perturbations.
Reachability Analysis of Neural Feedback Loops
While the solutions are less tight than previous (semidefinite program-based) methods, they are substantially faster to compute, and some of those computational time savings can be used to refine the bounds through new input set partitioning techniques, which is shown to dramatically reduce the tightness gap.
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians
Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing.
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers
Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems' safety properties.
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems
Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs.
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness.
R-MADDPG for Partially Observable Environments and Limited Communication
There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars.
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning
This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments.
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown.
Certified Adversarial Robustness for Deep Reinforcement Learning
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness.
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.
Planning Beyond the Sensing Horizon Using a Learned Context
Context is key information about structured environments that could guide exploration toward the unknown goal location, but the abstract idea is difficult to quantify for use in a planning algorithm.
Safe Reinforcement Learning with Model Uncertainty Estimates
The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e. g. goal) is unobservable to the others.
Multiagent Systems
