activity
20202025
most citedMaking Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors

18 citations · 39 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO20222 cited

NeurIPS 2022 Competition: Driving SMARTS

Amir Rasouli, Randy Goebel, Matthew E. Taylor +15

Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…

cs.CV20215 cited

Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects

Haoping Xu, Yi Ru Wang, Sagi Eppel +3

The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due ligh…

cs.LG2020

Latent Skill Planning for Exploration and Transfer

Kevin Xie, Homanga Bharadhwaj, Danijar Hafner +2

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environm…

cs.LG2020

Conservative Safety Critics for Exploration

Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart +3

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these pol…

cs.CY202018 cited

Making Sense of the Robotized Pandemic Response: A Comparison of Global and Canadian Robot Deployments and Success Factors

T. Barfoot, J. Burgner-Kahrs, E. Diller +12

From disinfection and remote triage, to logistics and delivery, countries around the world are making use of robots to address the unique challenges presented by the COVID-19 pande…

cs.LG202012 cited

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

Homanga Bharadhwaj, Kevin Xie, Florian Shkurti

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimizat…