For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention.
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
With the feedback of the observed trajectory, the algorithm is applied to neural-network-based models to improve the performance of driving behavior predictions across different human subjects and scenarios.
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV).
The proposed framework is applied to an artificial 2D dataset, the MNIST dataset, and a human motion dataset.
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC).
The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance.
Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control.
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers.
Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots).
Robotics Systems and Control
The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints.
Optimization and Control Robotics