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19952026
most citedDeep Recurrent Q-Learning for Partially Observable MDPs

687 citations · 3k across the 109 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.RO2023★ 2 cited

STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience

Haresh Karnan, Elvin Yang, Daniel Farkash +3

Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigatio…

cs.RO2023★ 1 cited

Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds

Amir Hossain Raj, Zichao Hu, Haresh Karnan +6

Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have develope…

cs.AI2023

Utilizing Mood-Inducing Background Music in Human-Robot Interaction

Elad Liebman, Peter Stone

Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot inte…

cs.RO2023

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 2nd BARN Challenge at ICRA 2023

Xuesu Xiao, Zifan Xu, Garrett Warnell +8

The 2nd BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, UK and continu…

cs.RO2023★ 4 cited

Deadlock-free, Safe, and Decentralized Multi-Robot Navigation in Social Mini-Games via Discrete-Time Control Barrier Functions

Rohan Chandra, Vrushabh Zinage, Efstathios Bakolas +2

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-robot systems operating in constrained environments, including doorways and intersections…

cs.RO2023

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

Xiaohan Zhang, Yifeng Zhu, Yan Ding +4

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space…