7 citations · 18 across the 7 of their papers we have counts for
9 papers
Neural Approaches to Co-Optimization in Robotics
Charles Schaff
Robots and intelligent systems that sense or interact with the world are increasingly being used to automate a wide array of tasks. The ability of these systems to complete these t…
N-LIMB: Neural Limb Optimization for Efficient Morphological Design
Charles Schaff, Matthew R. Walter
A robot's ability to complete a task is heavily dependent on its physical design. However, identifying an optimal physical design and its corresponding control policy is inherently…
Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer
Charles Schaff, Audrey Sedal, Matthew R. Walter
This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…
Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
Niklas Funk, Charles Schaff, Rishabh Madan +10
Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive en…
Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge
Takuma Yoneda, Charles Schaff, Takahiro Maeda +1
This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation compe…
Residual Policy Learning for Shared Autonomy
Charles Schaff, Matthew R. Walter
Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many…
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
Charles Schaff, David Yunis, Ayan Chakrabarti +1
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing numb…
Jointly Optimizing Placement and Inference for Beacon-based Localization
Charles Schaff, David Yunis, Ayan Chakrabarti +1
The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fi…