3 papers
cs.RO2021
Guiding Evolutionary Strategies by Differentiable Robot Simulators
Vladislav Kurenkov, Bulat Maksudov
In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms. Howev…
cs.RO2020
Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search
Vladislav Kurenkov, Hany Hamed, Sergei Savin
In this paper, we consider tensegrity hopper - a novel tensegrity-based robot, capable of moving by hopping. The paper focuses on the design of the stabilizing control policies, wh…
cs.CL2019
Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order
Vladislav Kurenkov, Bulat Maksudov, Adil Khan
In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple su…