12 citations · 18 across the 11 of their papers we have counts for
12 papers · 1 filter
Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Large Language Models (LLMs) are increasingly used to convert task commands into robot-executable code, however this pipeline lacks validation gates to detect unsafe and defective…
Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems
Taehyeon Kim, Vishnunandan L. N. Venkatesh, Byung-Cheol Min
Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial trajectory generation are tightly…
SafePlan: Leveraging Formal Logic and Chain-of-Thought Reasoning for Enhanced Safety in LLM-based Robotic Task Planning
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalit…
REBEL: Rule-based and Experience-enhanced Learning with LLMs for Initial Task Allocation in Multi-Human Multi-Robot Teaming
Arjun Gupte, Ruiqi Wang, Vishnunandan L. N. Venkatesh +4
Multi-human multi-robot teams are increasingly recognized for their efficiency in executing large-scale, complex tasks by integrating heterogeneous yet potentially synergistic huma…
Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods
Vishnunandan L. N. Venkatesh, Byung-Cheol Min
Learning from Demonstration (LfD) is a promising approach to enable Multi-Robot Systems (MRS) to acquire complex skills and behaviors. However, the intricate interactions and coord…
ZeroCAP: Zero-Shot Multi-Robot Context Aware Pattern Formation via Large Language Models
Vishnunandan L. N. Venkatesh, Byung-Cheol Min
Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatiall…
DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs
Gyeongmin Kim, Taehyeon Kim, Shyam Sundar Kannan +3
Mobile robots often rely on pre-existing maps for effective path planning and navigation. However, when these maps are unavailable, particularly in unfamiliar environments, a diffe…
SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
Shyam Sundar Kannan, Vishnunandan L. N. Venkatesh, Byung-Cheol Min
In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language…
UPPLIED: UAV Path Planning for Inspection through Demonstration
Shyam Sundar Kannan, Vishnunandan L. N. Venkatesh, Revanth Krishna Senthilkumaran +1
In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for I…
From the DESK (Dexterous Surgical Skill) to the Battlefield -- A Robotics Exploratory Study
Glebys T. Gonzalez, Upinder Kaur, Masudur Rahma +6
Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit f…
Inspection-on-the-fly using Hybrid Physical Interaction Control for Aerial Manipulators
Abbaraju Praveen, Xin Ma, Harikrishnan Manoj +3
Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, wi…
Extending Policy from One-Shot Learning through Coaching
Mythra V. Balakuntala, Vishnunandan L. N. Venkatesh, Jyothsna Padmakumar Bindu +2
Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations. The learnt task is corrected or extended to meet specific task goals by…