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Found 37 papers, 21 papers with code
An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic
Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations.
Safety Index Synthesis with State-dependent Control Space
This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces.
A Lightweight and Transferable Design for Robust LEGO Manipulation
LEGO is a well-known platform for prototyping pixelized objects.
Robotic Planning under Hierarchical Temporal Logic Specifications
In order to maximize the potential of LTL specifications, we capitalized on the intrinsic structure of tasks and introduced a hierarchical structure to LTL specifications.
State-wise Constrained Policy Optimization
In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO.
Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors.
GUARD: A Safe Reinforcement Learning Benchmark
Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms.
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments
This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples.
State-wise Safe Reinforcement Learning: A Survey
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges.
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning
Results show that the converged policies with intrinsic costs in all environments achieve zero constraint violation and comparable performance with baselines.
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022.
The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results
This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV).
Safe Control Under Input Limits with Neural Control Barrier Functions
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations.
General Place Recognition Survey: Towards the Real-world Autonomy Age
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
Loop Closure Detection
Simultaneous Localization and Mapping
BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas.
ARC -- Actor Residual Critic for Adversarial Imitation Learning
In AIL, an artificial adversary's misclassification is used as a reward signal that is optimized by any standard Reinforcement Learning (RL) algorithm.
An Optical Controlling Environment and Reinforcement Learning Benchmarks
Deep reinforcement learning has the potential to address various scientific problems.
Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method
For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention.
Transferable and Adaptable Driving Behavior Prediction
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.
Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
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.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
Safe Control with Neural Network Dynamic Models
It has been extensively studied regarding how to derive a safe control law with a control-affine analytical dynamic model.
The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results
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).
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC).
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
Online Verification of Deep Neural Networks under Domain Shift or Network Updates
Although neural networks are widely used, it remains challenging to formally verify the safety and robustness of neural networks in real-world applications.
Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming
Tolerance estimation problems are prevailing in engineering applications.
Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM
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).
Augmenting GAIL with BC for sample efficient imitation learning
Behavior cloning and GAIL are two widely used methods for performing imitation learning.
Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy
The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance.
Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
The effectiveness and flexibility of the proposed method has been validated in experiments.
Robotics
Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directions
In different methods, the energy function is called a potential function, a safety index, or a barrier function.
Algorithms for Verifying Deep Neural Networks
Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control.
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers.
Robot Safe Interaction System for Intelligent Industrial Co-Robots
Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots).
Robotics Systems and Control
The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning
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
