Search Results for author:
Found 5 papers, 2 papers with code
Learning a Diffusion Model Policy from Rewards via Q-Score Matching
However, previous works fail to exploit the score-based structure of diffusion models, and instead utilize a simple behavior cloning term to train the actor, limiting their ability in the actor-critic setting.
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.
Autonomously Untangling Long Cables
Cables are ubiquitous in many settings and it is often useful to untangle them.
DayDreamer: World Models for Physical Robot Learning
Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
