Ignat Georgiev — accepted AI-conference papers

2025

Learning Deployable Locomotion Control via Differentiable Simulation

Clemens Schwarke, Victor Klemm, Joshua Bagajo, Jean Pierre Sleiman, Ignat Georgiev, Jesus Tordesillas Torres +1

CoRL 2025poster

Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich tasks like locomotion is complicated by the inherently non-smooth nature of contact, impeding effective gradient-based optim…

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2025

PWM: Policy Learning with Multi-Task World Models

Ignat Georgiev, Varun Giridhar, Nicklas Hansen, Animesh Garg

ICLR 2025poster

Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World model methods offer scalability by learning a simulation of the environment but often rely on inefficient gradient-free optimization methods for policy ext…

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2024

Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation

Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden, Animesh Garg

ICML 2024poster

Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to zeroth-order gradient estimation, resulting in suboptimal policies. Conversely,…

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2020

Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing

Ignat Georgiev, Christoforos Chatzikomis, Timo Voelkl, Joshua Smith, Michael Mistry

CoRL 2020

Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model Predictive Controller (MPC). We present a novel non-linear semi-pa

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