109 citations · 270 across the 14 of their papers we have counts for
14 papers · 1 filter
RoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
Kaiyuan Chen, Shuangyu Xie, Uksang Yoo +9
It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, defo…
Achieving Human Level Competitive Robot Table Tennis
David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser +24
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first…
Robotic Table Tennis: A Case Study into a High Speed Learning System
David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan +32
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the abilit…
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
Inferring Articulated Rigid Body Dynamics from RGBD Video
Eric Heiden, Ziang Liu, Vibhav Vineet +2
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains whe…
Fast and Efficient Locomotion via Learned Gait Transitions
Yuxiang Yang, Tingnan Zhang, Erwin Coumans +2
We focus on the problem of developing energy efficient controllers for quadrupedal robots. Animals can actively switch gaits at different speeds to lower their energy consumption.…
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Daniel Seita, Pete Florence, Jonathan Tompson +4
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional co…
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
Eric Heiden, David Millard, Erwin Coumans +2
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation paramete…
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
Eric Heiden, David Millard, Erwin Coumans +1
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the…
Learning Agile Robotic Locomotion Skills by Imitating Animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang +3
Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. While manually-designed controllers have been able to emulate many com…
Policies Modulating Trajectory Generators
Atil Iscen, Ken Caluwaerts, Jie Tan +4
We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both…
Learning Fast Adaptation with Meta Strategy Optimization
Wenhao Yu, Jie Tan, Yunfei Bai +2
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-l…
Optimizing Simulations with Noise-Tolerant Structured Exploration
Krzysztof Choromanski, Atil Iscen, Vikas Sindhwani +2
We propose a simple drop-in noise-tolerant replacement for the standard finite difference procedure used ubiquitously in blackbox optimization. In our approach, parameter perturbat…
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Jie Tan, Tingnan Zhang, Erwin Coumans +5
Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leverag…