34 citations · 109 across the 13 of their papers we have counts for
14 papers
Rapid On-Robot Learning for Dynamic Manipulation Skills: Robot Juggling
Taeyoon Lee, Chunpeng Wang, Christopher G. Atkeson +2
We present an online learning framework that enables a bimanual robot to acquire diverse juggling patterns directly on physical hardware within minutes, even with a significant sim…
Soft Robotic Dynamic In-Hand Pen Spinning
Yunchao Yao, Uksang Yoo, Jean Oh +2
Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynam…
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…
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement
Nikolaos Gkanatsios, Ayush Jain, Zhou Xian +3
Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange. Our focus in this work is…
Shape from Shading for Robotic Manipulation
Arkadeep Narayan Chaudhury, Leonid Keselman, Christopher G. Atkeson
Controlling illumination can generate high quality information about object surface normals and depth discontinuities at a low computational cost. In this work we demonstrate a rob…
Learning Exploration Strategies to Solve Real-World Marble Runs
Alisa Allaire, Christopher G. Atkeson
Tasks involving locally unstable or discontinuous dynamics (such as bifurcations and collisions) remain challenging in robotics, because small variations in the environment can hav…
Using Memory-Based Learning to Solve Tasks with State-Action Constraints
Mrinal Verghese, Chris Atkeson
Tasks where the set of possible actions depend discontinuously on the state pose a significant challenge for current reinforcement learning algorithms. For example, a locked door m…
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…
Deep Kernels for Optimizing Locomotion Controllers
Rika Antonova, Akshara Rai, Christopher G. Atkeson
Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-ef…
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
Rika Antonova, Akshara Rai, Christopher G. Atkeson
Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gori…