activity
20112024
most citedInteractive Perception: Leveraging Action in Perception and Perception in Action

226 citations · 1k across the 102 of their papers we have counts for

collaborators
Showing 2019Show all

10 papers · 1 filter

cs.CV2019

Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Eric Heiden, Ziang Liu, Ragesh K. Ramachandran +1

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurement…

cs.RO2019

Resilience in multi-robot target tracking through reconfiguration

Ragesh K. Ramachandran, Nicole Fronda, Gaurav S. Sukhatme

We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing…

cs.RO2019

Resilient Coverage: Exploring the Local-to-Global Trade-off

Ragesh K. Ramachandran, Lifeng Zhou James A. Preiss, Gaurav S. Sukhatme

We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. I…

cs.LG2019

Meta-Learning via Learned Loss

Sarah Bechtle, Artem Molchanov, Yevgen Chebotar +4

Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper…

cs.LG2019★ 31 cited

Interactive Differentiable Simulation

Eric Heiden, David Millard, Hejia Zhang +1

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have con…

cs.RO2019★ 1 cited

Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors

Artem Molchanov, Tao Chen, Wolfgang Hönig +3

Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remar…