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Found 32 papers, 7 papers with code
A Meta-framework for Spatiotemporal Quantity Extraction from Text
News events are often associated with quantities (e. g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.
Large Language Models Can Plan Your Travels Rigorously with Formal Verification Tools
We evaluate our framework with TravelPlanner and achieve a success rate of 97%.
Large Language Models to the Rescue: Deadlock Resolution in Multi-Robot Systems
Multi-agent robotic systems are prone to deadlocks in an obstacle environment where the system can get stuck away from its desired location under a smooth low-level control policy.
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Our method combines the strength of CBF for real-time collision-avoidance control and RRT for long-horizon motion planning, by using CBF-induced neural controller (CBF-INC) to generate control signals that steer the system towards sampled configurations by RRT.
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp-down, an upcoming burning plasma scenario which we use as a testbed.
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Preference Alignment
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task.
Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals.
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help
We show that adversarial optimization is liable to severely overestimate the robustness of the optimized dispatch (when the adversary encounters a local minimum), leading the operator to falsely believe that their dispatch is secure.
Signal Temporal Logic Neural Predictive Control
We conduct experiments on six tasks, where our method with the backup policy outperforms the classical methods (MPC, STL-solver), model-free and model-based RL methods in STL satisfaction rate, especially on tasks with complex STL specifications while being 10X-100X faster than the classical methods.
Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration
LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models
Then, we finetune T5 models on the lifted versions (i. e., the specific Atomic Propositions (AP) are hidden) of the NL and TL.
Compositional Neural Certificates for Networked Dynamical Systems
Specifically, we treat a large networked dynamical system as an interconnection of smaller subsystems and develop methods that can find each subsystem a decentralized controller and an ISS Lyapunov function; the latter can be collectively composed to prove the global stability of the system.
Hybrid Systems Neural Control with Region-of-Attraction Planner
For each system mode, we first learn an NN Lyapunov function and an NN controller to ensure the states within the region of attraction (RoA) can be stabilized.
ConBaT: Control Barrier Transformer for Safe Policy Learning
In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion.
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.
Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning
Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online.
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.
SABLAS: Learning Safe Control for Black-box Dynamical Systems
Control certificates based on barrier functions have been a powerful tool to generate probably safe control policies for dynamical systems.
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov function, the existence of which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models.
Learning Density Distribution of Reachable States for Autonomous Systems
State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazardous situations.
Reactive and Safe Road User Simulations using Neural Barrier Certificates
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications.
Density Constrained Reinforcement Learning
We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works.
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
Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates
We propose a novel joint-learning framework that can be implemented in a decentralized fashion, with generalization guarantees for certain function classes.
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
Reactive motion planning with probabilistic safety guarantees
Motion planning in environments with multiple agents is critical to many important autonomous applications such as autonomous vehicles and assistive robots.
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference
Our technique uses model simulations to learn {\em surrogate models}, and uses {\em conformal inference} to provide probabilistic guarantees on the satisfaction of a given STL property.
Partial Or Complete, That's The Question
For many structured learning tasks, the data annotation process is complex and costly.
Exploiting Partially Annotated Data in Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
Exploiting Partially Annotated Data for Temporal Relation Extraction
As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena.
