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Found 14 papers, 6 papers with code
Robotic Table Tennis: A Case Study into a High Speed Learning System
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 ability to precisely return the ball to desired targets.
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states.
Differentiable Dynamics for Articulated 3d Human Motion Reconstruction
We introduce DiffPhy, a differentiable physics-based model for articulated 3d human motion reconstruction from video.
Ranked #58 on
3D Human Pose Estimation
on Human3.6M
Inferring Articulated Rigid Body Dynamics from RGBD Video
Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications.
Fast and Efficient Locomotion via Learned Gait Transitions
We focus on the problem of developing energy efficient controllers for quadrupedal robots.
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag".
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
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 parameters that best fit the observed sensor readings.
Robotics
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
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 computation.
Learning Agile Robotic Locomotion Skills by Imitating Animals
In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
Policies Modulating Trajectory Generators
We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.
Learning Fast Adaptation with Meta Strategy Optimization
The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
The control policies are learned in a physics simulator and then deployed on real robots.
