4 papers
Predictive Style Matching: Natural and Robust Humanoid Locomotion
Simeon Nedelchev, Ekaterina Chaikovskaia, Egor Davydenko +2
Reinforcement learning has become the prevailing approach to humanoid locomotion control: policies transfer reliably from simulation to hardware and recover gracefully from disturb…
Trajectory-based actuator identification via differentiable simulation
Vyacheslav Kovalev, Ekaterina Chaikovskaia, Egor Davydenko +1
Accurate actuation models are critical for bridging the gap between simulation and real robot behavior, yet obtaining high-fidelity actuator dynamics typically requires dedicated t…
Heuristic Step Planning for Learning Dynamic Bipedal Locomotion: A Comparative Study of Model-Based and Model-Free Approaches
William Suliman, Ekaterina Chaikovskaia, Egor Davydenko +1
This work presents an extended framework for learning-based bipedal locomotion that incorporates a heuristic step-planning strategy guided by desired torso velocity tracking. The f…
Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification
Vyacheslav Kovalev, Ekaterina Chaikovskaia, Egor Davydenko +1
Accurate system identification is crucial for reducing trajectory drift in bipedal locomotion, particularly in reinforcement learning and model-based control. In this paper, we pre…