138 citations · 140 across the 9 of their papers we have counts for
10 papers
Coachable Agents for Interactive Gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing (Silver et al., 2016; 2017;…
GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
Vassil Atanassov, Wanming Yu, Siddhant Gangapurwala +2
Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to…
Roll-Drop: accounting for observation noise with a single parameter
Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala +2
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 noi…
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
Siddhant Gangapurwala, Luigi Campanaro, Ioannis Havoutis
Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challenge this intuitive notion by demonstra…
Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization
Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt +1
Training deep reinforcement learning (DRL) locomotion policies often require massive amounts of data to converge to the desired behaviour. In this regard, simulators provide a chea…
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert +5
Quadruped locomotion is rapidly maturing to a degree where robots are able to realise highly dynamic manoeuvres. However, current planners are unable to vary key gait parameters of…
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert +5
Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecti…
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini +2
Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Alth…
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino +2
We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive…
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion
Siddhant Gangapurwala, Alexander Mitchell, Ioannis Havoutis
Deep reinforcement learning (RL) uses model-free techniques to optimize task-specific control policies. Despite having emerged as a promising approach for complex problems, RL is s…