27 citations · 64 across the 5 of their papers we have counts for
5 papers
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier +7
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…
Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer
Rae Jeong, Jackie Kay, Francesco Romano +6
Learning robotic control policies in the real world gives rise to challenges in data efficiency, safety, and controlling the initial condition of the system. On the other hand, sim…
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Rae Jeong, Yusuf Aytar, David Khosid +5
Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time…
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…
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
Devin Schwab, Tobias Springenberg, Murilo F. Martins +7
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…