34 citations · 62 across the 3 of their papers we have counts for
6 papers
Incorporating dense metric depth into neural 3D representations for view synthesis and relighting
Arkadeep Narayan Chaudhury, Igor Vasiljevic, Sergey Zakharov +4
Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulati…
Using Collocated Vision and Tactile Sensors for Visual Servoing and Localization
Arkadeep Narayan Chaudhury, Timothy Man, Wenzhen Yuan +1
Coordinating proximity and tactile imaging by collocating cameras with tactile sensors can 1) provide useful information before contact such as object pose estimates and visually s…
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
Sebastian Höfer, Kostas Bekris, Ankur Handa +12
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference. Twel…
Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped
Tianyu Li, Akshara Rai, Hartmut Geyer +1
Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, d…
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Akshara Rai, Rika Antonova, Franziska Meier +1
Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically requi…
Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped
Akshara Rai, Rika Antonova, Seungmoon Song +3
Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but…