59 citations · 59 across the 4 of their papers we have counts for
4 papers
SafeExplorer: An Unbiased Mixed-Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause damage through falls, so minimiz…
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand, Shafeef Omar, Glen Berseth +2
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is…
A unified framework for walking and running of bipedal robots
Mahrokh Ghoddousi Boroujeni, Elham Daneshmand, Ludovic Righetti +1
In this paper, we propose a novel framework capable of generating various walking and running gaits for bipedal robots. The main goal is to relax the fixed center of mass (CoM) hei…
Variable Horizon MPC with Swing Foot Dynamics for Bipedal Walking Control
Elham Daneshmand, Majid Khadiv, Felix Grimminger +1
In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the la…