4 citations · 9 across the 12 of their papers we have counts for
13 papers · 1 filter
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…