4 citations · 9 across the 12 of their papers we have counts for
14 papers
Persistent LLM Orchestration of Heterogeneous Robot Teams via Partial Delegation to Multi-Agent Protocols
Maria G. Mendoza, Victoria M. Tuck, Zachary Ravichandran +2
Natural language specifications let non-expert users task robot teams, and large language models (LLMs) have recently proven effective at grounding such semantic specifications int…
Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language
Zachary Ravichandran, Jonathan Diller, Fernando Cladera +3
Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned policies assume that goals are…
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
Zachary Ravichandran, Fernando Cladera, Ankit Prabhu +6
While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large language models (LLMs) and vision lang…
Adversarial Attacks on Robotic Vision Language Action Models
Eliot Krzysztof Jones, Alexander Robey, Andy Zou +5
The emergence of vision-language-action models (VLAs) for end-to-end control is reshaping the field of robotics by enabling the fusion of multimodal sensory inputs at the billion-p…
Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
Zachary Ravichandran, Ignacio Hounie, Fernando Cladera +3
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-ho…
Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities
Zachary Ravichandran, Fernando Cladera, Jason Hughes +5
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments. However, existing F…
Air-Ground Collaboration for Language-Specified Missions in Unknown Environments
Fernando Cladera, Zachary Ravichandran, Jason Hughes +6
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…
Safety Guardrails for LLM-Enabled Robots
Zachary Ravichandran, Alexander Robey, Vijay Kumar +2
Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from av…
Jailbreaking LLM-Controlled Robots
Alexander Robey, Zachary Ravichandran, Vijay Kumar +2
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 a…
SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments
Zachary Ravichandran, Varun Murali, Mariliza Tzes +2
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…
Challenges and Opportunities for Large-Scale Exploration with Air-Ground Teams using Semantics
Fernando Cladera, Ian D. Miller, Zachary Ravichandran +5
One common and desirable application of robots is exploring potentially hazardous and unstructured environments. Air-ground collaboration offers a synergistic approach to addressin…
Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications
Fernando Cladera, Zachary Ravichandran, Ian D. Miller +3
Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support…
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks
Zachary Ravichandran, Lisa Peng, Nathan Hughes +2
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…
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments
Zachary Ravichandran, J. Daniel Griffith, Benjamin Smith +1
Significant progress has been made in scene understanding which seeks to build 3D, metric and object-oriented representations of the world. Concurrently, reinforcement learning has…