76 citations · 116 across the 44 of their papers we have counts for
48 papers
Navigate or Relocate? Planning Among Movable Obstacles in Unknown Environments
Yuqing Zhang, Haoyu Zhu, Yiannis Kantaros
Conventional robot planning methods seek collision-free paths to a goal but fail when all paths are blocked. In these cases, the robot must determine which objects to relocate, in…
Legislating World-Model-Based Planning with Legal Reasoning
Dylan Waldner, Yiannis Kantaros, Guido Governatori +2
As robotic systems grow more general, legal norms are needed to integrate them into society. This paper extends the isomorphism problem of aligning legal source texts with their en…
Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics
Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros
Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to de…
Conformal Reachability for Safe Control in Unknown Environments
Xinhang Ma, Junlin Wu, Yiannis Kantaros +1
Designing provably safe control is a core problem in trustworthy autonomy. However, most prior work in this regard assumes either that the system dynamics are known or deterministi…
Making Infeasible Tasks Feasible: Planning to Reconfigure Disconnected 3D Environments with Movable Objects
Samarth Kalluraya, Yiannis Kantaros
Several planners have been developed to compute dynamically feasible, collision-free robot paths from an initial to a goal configuration. A key assumption in these works is that th…
Deadlock-Free Hybrid RL-MAPF Framework for Zero-Shot Multi-Robot Navigation
Haoyi Wang, Licheng Luo, Yiannis Kantaros +2
Multi-robot navigation in cluttered environments presents fundamental challenges in balancing reactive collision avoidance with long-range goal achievement. When navigating through…
CoFineLLM: Conformal Finetuning of LLMs for Language-Instructed Robot Planning
Jun Wang, Yevgeniy Vorobeychik, Yiannis Kantaros
Large Language Models (LLMs) have recently emerged as planners for language-instructed agents, generating sequences of actions to accomplish natural language tasks. However, their…
Conformalized Non-uniform Sampling Strategies for Accelerated Sampling-based Motion Planning
Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros
Sampling-based motion planners (SBMPs) are widely used to compute dynamically feasible robot paths. However, their reliance on uniform sampling often leads to poor efficiency and s…
Safe Planning in Unknown Environments Using Conformalized Semantic Maps
David Smith Sundarsingh, Yifei Li, Tianji Tang +3
This paper addresses semantic planning problems in unknown environments under perceptual uncertainty. The environment contains multiple unknown semantically labeled regions or obje…
NAMO-LLM: Efficient Navigation Among Movable Obstacles with Large Language Model Guidance
Yuqing Zhang, Yiannis Kantaros
Several planners have been proposed to compute robot paths that reach desired goal regions while avoiding obstacles. However, these methods fail when all pathways to the goal are b…
HyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural Networks
Jonathan Gornet, Yiannis Kantaros, Bruno Sinopoli
We introduce Hyperparameter Controller (HyperController), a computationally efficient algorithm for hyperparameter optimization during training of reinforcement learning neural net…
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Vivian Lin, Ramneet Kaur, Yahan Yang +6
The safety of learning-enabled cyber-physical systems is compromised by the well-known vulnerabilities of deep neural networks to out-of-distribution (OOD) inputs. Existing literat…
ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees
David Smith Sundarsingh, Jun Wang, Jyotirmoy V. Deshmukh +1
Linear Temporal Logic (LTL) is a widely used task specification language for autonomous systems. To mitigate the significant manual effort and expertise required to define LTL-enco…
Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees
Xinhang Ma, Junlin Wu, Hussein Sibai +2
Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship betwe…
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration
Yiannis Kantaros, Jun Wang
This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics and high-level control objectives specified as Linear Temporal Logic (LTL)…
Minimum-Violation Temporal Logic Planning for Heterogeneous Robots under Robot Skill Failures
Samarth Kalluraya, Beichen Zhou, Yiannis Kantaros
In this paper, we consider teams of robots with heterogeneous skills (e.g., sensing and manipulation) tasked with collaborative missions described by Linear Temporal Logic (LTL) fo…
Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction
Jun Wang, Guocheng He, Yiannis Kantaros
This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities at v…
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis
Rohan Mitta, Hosein Hasanbeig, Jun Wang +3
This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during le…
Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks
Jun Wang, Haojun Chen, Zihe Sun +1
Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captu…
Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications
Jun Wang, Hosein Hasanbeig, Kaiyuan Tan +2
This paper addresses the problem of designing control policies for agents with unknown stochastic dynamics and control objectives specified using Linear Temporal Logic (LTL). Recen…
Conformal Temporal Logic Planning using Large Language Models
Jun Wang, Jiaming Tong, Kaiyuan Tan +2
This paper addresses planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a te…
Uncertainty-bounded Active Monitoring of Unknown Dynamic Targets in Road-networks with Minimum Fleet
Shuaikang Wang, Yiannis Kantaros, Meng Guo
Fleets of unmanned robots can be beneficial for the long-term monitoring of large areas, e.g., to monitor wild flocks, detect intruders, search and rescue. Monitoring numerous dyna…
Neural Lyapunov Control for Discrete-Time Systems
Junlin Wu, Andrew Clark, Yiannis Kantaros +1
While ensuring stability for linear systems is well understood, it remains a major challenge for nonlinear systems. A general approach in such cases is to compute a combination of…
Resilient Temporal Logic Planning in the Presence of Robot Failures
Samarth Kalluraya, George J. Pappas, Yiannis Kantaros
Several task and motion planning algorithms have been proposed recently to design paths for mobile robot teams with collaborative high-level missions specified using formal languag…