Search Results for author:
Found 7 papers, 0 papers with code
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale.
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.
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.
System Analysis and Test-Bed for an Atmosphere-Breathing Electric Propulsion System using an Inductive Plasma Thruster
An atmosphere-breathing electric propulsion system (ABEP) ingests the residual atmosphere particles through an intake and uses them as propellant for an electric thruster.
Space Physics
A Review of Gas-Surface Interaction Models for Orbital Aerodynamics Applications
Limits and assets of each model have been discussed with regards to the context of this paper.
Space Physics
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system.
