no code implementations • 25 Apr 2023 • Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis
This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state.
3 code implementations • 29 Sep 2022 • Siddhant Gangapurwala, Luigi Campanaro, Ioannis Havoutis
Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency.
no code implementations • 26 Sep 2022 • Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis
This allows us to obtain locomotion policies that are robust to variations in system dynamics.
no code implementations • 2 May 2022 • Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner
We evaluate our approach on two versions of the real ANYmal quadruped robots and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.
no code implementations • 9 Dec 2021 • Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones, Ioannis Havoutis, Ingmar Posner
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
no code implementations • 25 Feb 2021 • Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini, Wolfgang Merkt, Ioannis Havoutis
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
Robotics
1 code implementation • 5 Dec 2020 • Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino, Maurice Fallon, Ioannis Havoutis
We evaluate the robustness of our method over a wide variety of complex terrains.
no code implementations • 3 Jul 2020 • Alexander L. Mitchell, Martin Engelcke, Oiwi Parker Jones, David Surovik, Siddhant Gangapurwala, Oliwier Melon, Ioannis Havoutis, Ingmar Posner
In addition, kinodynamic constraints are often non-differentiable and difficult to implement in an optimisation approach.
no code implementations • 22 Feb 2020 • Siddhant Gangapurwala, Alexander Mitchell, Ioannis Havoutis
Deep reinforcement learning (RL) uses model-free techniques to optimize task-specific control policies.