Zachary Ravichandran — accepted AI-conference papers

2026

Air-Ground Collaboration for Language-Specified Missions in Unknown Environments (I)

Fernando Cladera, Zachary Ravichandran, Jason Hughes, Varun Murali, Carlos Nieto-Granda, M. Ani Hsieh +3

ICRA 2026poster

As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming si…

Cited by 0 Scholar ↗

2026

Safety Guardrails for LLM-Enabled Robots

Zachary Ravichandran, Alexander Robey, Vijay Kumar, George J. Pappas, Hamed Hassani

RA-L 2026

Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"

Cited by 30 Source ↗code ↗Scholar ↗

2025

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention

Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar

CoRL 2025poster

Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or i…

Cited by 0 Source ↗Scholar ↗

2025

Jailbreaking LLM-Controlled Robots

Alexander Robey, Zachary Ravichandran, Vijay Kumar, Hamed Hassani, George J. Pappas

ICRA 2025

The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When viewed as a stand-alone technology, LLMs are known

Cited by 70 Source ↗Scholar ↗

2025

SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments

Zachary Ravichandran, Varun Murali, Mariliza Tzes, George J. Pappas, Vijay Kumar

ICRA 2025

As robots become increasingly capable, users will want to describe high-level missions and have robots infer the relevant details. Because pre-built maps are difficult to obtain in many realistic settings, accomplishing such missions will require the robot to map and plan online. While many semantic

Cited by 25 Source ↗code ↗Scholar ↗

2024

Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications

Fernando Cladera, Zachary Ravichandran, Ian D. Miller, M. Ani Hsieh, C. J. Taylor, Vijay Kumar

ICRA 2024poster

Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support complex tasks must be robust to unreliable and intermittent communication links. In this work, we present MOCHA (Multi-r…

Cited by 7 Source ↗Scholar ↗

2022

Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks

Zachary Ravichandran, Lisa Peng, Nathan Hughes, J. Daniel Griffith, Luca Carlone

ICRA 2022poster

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB imag…

Cited by 88 Source ↗code ↗Scholar ↗