166 citations · 714 across the 35 of their papers we have counts for
5 papers · 1 filter
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever +8
For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an…
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner
Philemon Brakel, Steven Bohez, Leonard Hasenclever +2
Dynamic quadruped locomotion over challenging terrains with precise foot placements is a hard problem for both optimal control methods and Reinforcement Learning (RL). Non-linear s…
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…
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…