1 citations · 1 across the 19 of their papers we have counts for
9 papers · 1 filter
Personalized Embodied Navigation for Portable Object Finding
Vishnu Sashank Dorbala, Bhrij Patel, Amrit Singh Bedi +1
Embodied navigation methods commonly operate in static environments with stationary objects. In this work, we present approaches for tackling navigation in dynamic scenarios with n…
SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
Xiyang Wu, Guangyao Shi, Qingzi Wang +3
Vision-language-action (VLA) models enable robots to follow natural-language instructions grounded in visual observations, but the instruction channel also introduces a critical vu…
EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering
Kai Cheng, Zhengyuan Li, Xingpeng Sun +3
Embodied Question Answering (EQA) is an essential yet challenging task for robot assistants. Large vision-language models (VLMs) have shown promise for EQA, but existing approaches…
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5
In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…
On the Vulnerability of LLM/VLM-Controlled Robotics
Xiyang Wu, Souradip Chakraborty, Ruiqi Xian +6
In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities. While…
REBEL: Reward Regularization-Based Approach for Robotic Reinforcement Learning from Human Feedback
Souradip Chakraborty, Anukriti Singh, Amisha Bhaskar +3
The effectiveness of reinforcement learning (RL) agents in continuous control robotics tasks is mainly dependent on the design of the underlying reward function, which is highly pr…