Search Results for author: Armin Biess

Found 7 papers, 3 papers with code

Example-guided learning of stochastic human driving policies using deep reinforcement learning

1 code implementation Neural Computing and Applications 2022 Ran Emuna, Rotem Duffney, Avinoam Borowsky, Armin Biess

Here we introduce a model-free and easy-to-implement deep reinforcement learning approach to mimic the stochastic behavior of a human expert by learning distributions of task variables from examples.

Autonomous Vehicles Gaussian Processes +3

Curriculum Learning with Hindsight Experience Replay for Sequential Object Manipulation Tasks

no code implementations21 Aug 2020 Binyamin Manela, Armin Biess

Learning complex tasks from scratch is challenging and often impossible for humans as well as for artificial agents.

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

1 code implementation7 Jun 2020 Ran Emuna, Avinoam Borowsky, Armin Biess

However, human-controlled vehicles are likely to remain on the roads for several decades to come and may share with AVs the traffic environments of the future.

Collision Avoidance Gaussian Processes +2

Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms

no code implementations25 Feb 2020 Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess

The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential.

Imitation Learning Vocal Bursts Valence Prediction

Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization

1 code implementation14 May 2019 Binyamin Manela, Armin Biess

We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals.

Multi-Goal Reinforcement Learning

Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images

no code implementations27 Sep 2018 Yuval Litvak, Armin Biess, Aharon Bar-Hillel

We obtain an average pose estimation error of 2. 16 millimeters and 0. 64 degree leading to 91% success rate for robotic assembly of randomly distributed parts.

Pose Estimation

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