3 citations · 3 across the 13 of their papers we have counts for
13 papers
Diversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics Datasets
Sreevardhan Sirigiri, Nathan Samuel de Lara, Christopher Agia +2
Robotics datasets for imitation learning typically consist of long-horizon trajectories of different lengths over states, actions, and high-dimensional observations (e.g., RGB vide…
Deployment-Time Reliability of Learned Robot Policies
Christopher Agia
Recent advances in learning-based robot manipulation have produced policies with remarkable capabilities. Yet, reliability at deployment remains a fundamental barrier to real-world…
Preventing Robotic Jailbreaking via Multimodal Domain Adaptation
Francesco Marchiori, Rohan Sinha, Christopher Agia +4
Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to jailbreaking attacks that bypass safety me…
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
Jacky Kwok, Christopher Agia, Rohan Sinha +5
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in visuomotor control, yet ensuring their robustness in unstructured real-world environments remains a…
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang +5
In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…
Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning
Milan Ganai, Rohan Sinha, Christopher Agia +3
While foundation models offer promise toward improving robot safety in out-of-distribution (OOD) scenarios, how to effectively harness their generalist knowledge for real-time, dyn…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress
Christopher Agia, Rohan Sinha, Jingyun Yang +4
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies a…
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia +6
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Nove…
Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction
Jakob Thumm, Christopher Agia, Marco Pavone +1
Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approache…
Real-Time Anomaly Detection and Reactive Planning with Large Language Models
Rohan Sinha, Amine Elhafsi, Christopher Agia +3
Foundation models, e.g., large language models (LLMs), trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards de…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
Modeling Considerations for Developing Deep Space Autonomous Spacecraft and Simulators
Christopher Agia, Guillem Casadesus Vila, Saptarshi Bandyopadhyay +9
To extend the limited scope of autonomy used in prior missions for operation in distant and complex environments, there is a need to further develop and mature autonomy that jointl…