5k citations · 11k across the 51 of their papers we have counts for
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Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler, Felix Widmaier, Cansu Sancaktar +21
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and be…
Towards A Unified Agent with Foundation Models
Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever +3
Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and pla…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains
Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan +5
In this paper we study the problem of learning multi-step dynamics prediction models (jumpy models) from unlabeled experience and their utility for fast inference of (high-level) p…
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
Alex X. Lee, Coline Devin, Yuxiang Zhou +18
We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategie…
Evaluating model-based planning and planner amortization for continuous control
Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim +8
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this i…
"What, not how": Solving an under-actuated insertion task from scratch
Giulia Vezzani, Michael Neunert, Markus Wulfmeier +7
Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics…
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
Roland Hafner, Tim Hertweck, Philipp Klöppner +6
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 t…
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki +6
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 le…
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
Rico Jonschkowski, Roland Hafner, Jonathan Scholz +1
We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects. PVEs encode a single image i…
Learning and Transfer of Modulated Locomotor Controllers
Nicolas Heess, Greg Wayne, Yuval Tassa +3
We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor pr…