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Roland Hafner

14 accepted papers

2025

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins +10
ICRA 2025

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces

Cited by 12Source ↗Scholar ↗
2024

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots

Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch +9
ICRA 2024poster

Reinforcement learning solely from an agent’s self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal d…

Cited by 6Source ↗Scholar ↗
2024

Offline Actor-Critic Reinforcement Learning Scales to Large Models

Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe +6
ICML 2024oral

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on…

Cited by 17Source ↗PDF ↗Scholar ↗
2024

The Design of the Barkour Benchmark for Robot Agility

Wenhao Yu, Ken Caluwaerts, Atil Iscen, J. Chase Kew, Tingnan Zhang, Daniel Freeman +33
IROS 2024poster

In this paper, we describe the design of the Barkour benchmark for measuring robot agility in navigating complex environments. Despite the growing interest in developing agile robot locomotion skills, the field lacks systematic benchmarks to measure the performance of robotic control systems and har…

Cited by 1Source ↗Scholar ↗
2021

Data-efficient Hindsight Off-policy Option Learning

Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck +5
ICML 2021spotlight

We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the dynamic programming inference procedure to robustly train all policy components off-policy and end-to-end. The approach o…

Cited by 52Source ↗PDF ↗Scholar ↗
2021

Representation Matters: Improving Perception and Exploration for Robotics

Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni +5
ICRA 2021poster

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this que…

Cited by 17Source ↗Scholar ↗
2021

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion

Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan +9
CoRL 2021poster

Algorithms for self-learning systems have made considerable progress in recent years, yet safety concerns and the need for additional instrumentation have so far largely limited learning experiments with real robots to well controlled lab settings. In this paper, we demonstrate how a small bipedal r…

Cited by 24Source ↗Scholar ↗
2020

Compositional Transfer in Hierarchical Reinforcement Learning

Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Noah Siegel +4
RSS 2020poster

The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements. We introduce Regularized Hierarchical Policy Optimization (RHPO) to improve data-efficiency for domains with multiple dominant tasks and ultim…

Cited by 41Source ↗PDF ↗Scholar ↗
2020

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning

Noah Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe +3
ICLR 2020poster

Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In pract…

Cited by 330Source ↗Scholar ↗
2020

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion

Roland Hafner, Tim Hertweck, Philipp Kloeppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier +3
CoRL 2020

Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that they can represent a general class of methods that allow to learn a solution with a reasonably set reward and minimal pri

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Tobias Springenberg, Roland Hafner +4
CoRL 2019

Many real-world control problems involve both discrete decision variables – such as the choice of control modes, gear switching or digital outputs – as well as continuous decision variables – such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding opti

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models

Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe +3
CoRL 2019

Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to new tasks. In this paper, we explore how model-based Reinforcement Learning (RL) can f

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Simultaneously Learning Vision and Feature-Based Control Policies for Real-World Ball-In-A-Cup

Devin Schwab, Jost Tobias Springenberg, Murilo Fernandes Martins, Michael Neunert, Thomas Lampe, Abbas Abdolmaleki +4
RSS 2019poster

We present a method for fast training of vision based control policies on real robots. The key idea behind our method is to perform multi-task Reinforcement Learning with auxiliary tasks that differ not only in the reward to be optimized but also in the state-space in which they operate. In particul…

Cited by 31Source ↗PDF ↗Scholar ↗
2018

Learning by Playing Solving Sparse Reward Tasks from Scratch

Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Wiele +3
ICML 2018oral

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary ta…

Cited by 549Source ↗PDF ↗code ↗Scholar ↗