80 citations · 283 across the 19 of their papers we have counts for
20 papers
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +41
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify…
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman, Martin Weiss, Manuel Wüthrich +4
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limi…
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…
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Nico Gürtler, Sebastian Blaes, Pavel Kolev +5
Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular…
Optimal Engagement-Diversity Tradeoffs in Social Media
Fabian Baumann, Daniel Halpern, Ariel D. Procaccia +3
Social media platforms are known to optimize user engagement with the help of algorithms. It is widely understood that this practice gives rise to echo chambers\emdash users are ma…
Representation with Incomplete Votes
Daniel Halpern, Gregory Kehne, Ariel D. Procaccia +2
Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with…
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy +7
This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipula…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
Arthur Allshire, Mayank Mittal, Varun Lodaya +7
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym sim…
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents
Andrea Dittadi, Frederik Träuble, Manuel Wüthrich +7
Building sample-efficient agents that generalize out-of-distribution (OOD) in real-world settings remains a fundamental unsolved problem on the path towards achieving higher-level…
Regret Bounds for Gaussian-Process Optimization in Large Domains
Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause
The goal of this paper is to characterize Gaussian-Process optimization in the setting where the function domain is large relative to the number of admissible function evaluations,…
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Ossama Ahmed, Frederik Träuble, Anirudh Goyal +5
Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing thi…
On the Transfer of Disentangled Representations in Realistic Settings
Andrea Dittadi, Frederik Träuble, Francesco Locatello +5
Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disent…
S2RMs: Spatially Structured Recurrent Modules
Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal +5
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the inpu…
Real-time Perception meets Reactive Motion Generation
Daniel Kappler, Franziska Meier, Jan Issac +7
We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-pred…
Probabilistic Articulated Real-Time Tracking for Robot Manipulation
Cristina Garcia Cifuentes, Jan Issac, Manuel Wüthrich +2
We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. Th…
Depth-Based Object Tracking Using a Robust Gaussian Filter
Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes +3
We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this pro…
Robust Gaussian Filtering using a Pseudo Measurement
Manuel Wüthrich, Cristina Garcia Cifuentes, Sebastian Trimpe +4
Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor…
The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems
Manuel Wüthrich, Jeannette Bohg, Daniel Kappler +2
Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if t…
Probabilistic Object Tracking using a Range Camera
Manuel Wüthrich, Peter Pastor, Mrinal Kalakrishnan +2
We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compu…