Proceedings
Panel
Panel Questions
Please post your questions and any discussion in this slack channel by June 10 for inclusion in our panel discussion with invited speakers (joining instructions are available after registering here or via the ICRA slack). Our panel will consist of:
- Nathan Ratliff (NVIDIA)
- Aude Billard (EPFL)
- Sergey Levine (UC Berkeley)
- Michael Beetz (University Bremen)
- Marc Toussaint (TU Berline)
- Emre Ugur (Bogazici University)
Invited Talks
Sergey Levine (UC Berkeley), Model-based RL
Nathan Ratliff (NVIDIA) , Optimization Over a Geometric Fabric
Michael Beetz (University Bremen), Learning human scale manipulation tasks
Accepted Papers and Talks
All the papers are available in this Google Drive, the talks are on this Youtube Playlist, and the discussion can be found at this Slack Workspace (joining instructions are available after registering here or via the ICRA slack). Paper Q&As are available in channels ws20_01, ws20_02, ..., ws20_17.
Closed-loop behaviour of learned models in robotic food-cutting
by Ioanna Mitsioni (KTH); Yiannis Karayiannidis (Chalmers University of Technology); Danica Kragic (KTH)
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations
by Rogerio Bonatti (CMU); Ratnesh Madaan (Microsoft); Vibhav Vineet (Microsoft Research); Sebastian Scherer (CMU); Ashish Kapoor (Microsoft)
Time-Informed Exploration For Robot Motion Planning
by Sagar S Joshi (Georgia Institute of Technology); Seth Hutchinson (Georgia Tech); Panagiotis Tsiotras (Georgia Institute of Technology)
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
by Iman Nematollahi (University of Freiburg); Oier Mees (University of Freiburg); Lukas Hermann (University of Freiburg); Wolfram Burgard (University of Freiburg)
Learning to Fly via Deep Model-Based Reinforcement Learning
by Philip Becker-Ehmck (Volkswagen Group); Maximilian Karl (Machine Learning Research Lab, Volkswagen Group); Jan Peters (TU Darmstadt); Patrick van der Smagt Volkswagen Group)
DISCO: Double Likelihood-free Inference Stochastic Control
by Lucas Barcelos (The University of Sydney); Rafael Oliveira (The University of Sydney); Rafael Possas (University of Sydney); Lionel Ott (The University of Sydney); Fabio Ramos (NVIDIA, The University of Sydney)
APPLD: Adaptive Planner Parameter Learning from Demonstration
by Xuesu Xiao (UT Austin); Bo Liu (UT Austin); Garrett Warnell (US Army Research Laboratory); Jonathan Fink (US Army Research Lab); Peter Stone (UT Austin)
Lazy Action Value Models for Q-Learning in Robotics
by Ingmar F Schubert (TU Berlin); Marc Toussaint (TU Berlin and Max Planck)
Visual Navigation Among Humans With Optimal Control as A Supervisor
by Varun Tolani (UC Berkeley); Somil Bansal (UC Berkeley); Aleksandra Faust (Google Brain); Claire Tomlin (UC Berkeley)
Stein Variational Model Predictive Control
by Alexander Lambert (Georgia Institute of Technology); Adam Fishman (U Washington); Dieter Fox (NVIDIA Research / U Washington); Byron Boots (U Washington); Fabio Ramos (U Sydney / NVIDIA)
Data-efficient Control from Images by Learning How to Use a Simple Model
by Thomas J Power (U Michigan); Dmitry Berenson (U Michigan)
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image
by Danny Driess (University of Stuttgart); Jung-Su Ha (Jung-Su Ha); Marc Toussaint (TU Berlin)
Robotic Motion Planning using Learned Critical Sources and Local Sampling
by Rajat K Jenamani (IIT Kharagpur); Rahul Kumar (IIT Kharagpur); Parth Mall (IIT Kharagpur); Kushal Kedia (IIT Kharagpur)
Learning When to Trust a Dynamics Model When Planning With Physical Constraints
by Peter Mitrano (U Michigan); Dale McConachie (U Michigan); Dmitry Berenson (U Michigan)