Search Results for author: Akansel Cosgun

Found 19 papers, 4 papers with code

Passing Through Narrow Gaps with Deep Reinforcement Learning

no code implementations6 Mar 2021 Brendan Tidd, Akansel Cosgun, Jurgen Leitner, Nicolas Hudson

While we show the feasibility of our approach in simulation, the difference in performance between simulated and real world scenarios highlight the difficulty of direct sim-to-real transfer for deep reinforcement learning policies.

reinforcement-learning Reinforcement Learning (RL)

Learning Setup Policies: Reliable Transition Between Locomotion Behaviours

no code implementations23 Jan 2021 Brendan Tidd, Nicolas Hudson, Akansel Cosgun, Jurgen Leitner

Dynamic platforms that operate over many unique terrain conditions typically require many behaviours.

Semantics for Robotic Mapping, Perception and Interaction: A Survey

no code implementations2 Jan 2021 Sourav Garg, Niko Sünderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke, Michael Milford

In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning.

Autonomous Driving Navigate

Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts

no code implementations1 Nov 2020 Brendan Tidd, Nicolas Hudson, Akansel Cosgun, Jurgen Leitner

Legged robots often use separate control policiesthat are highly engineered for traversing difficult terrain suchas stairs, gaps, and steps, where switching between policies isonly possible when the robot is in a region that is commonto adjacent controllers.

Guided Curriculum Learning for Walking Over Complex Terrain

no code implementations8 Oct 2020 Brendan Tidd, Nicolas Hudson, Akansel Cosgun

Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning.

Strawberry Detection using Mixed Training on Simulated and Real Data

no code implementations24 Aug 2020 Sunny Goondram, Akansel Cosgun, Dana Kulic

This paper demonstrates how simulated images can be useful for object detection tasks in the agricultural sector, where labeled data can be scarce and costly to collect.

object-detection Object Detection

Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

1 code implementation2 Jun 2020 Patrick Rosenberger, Akansel Cosgun, Rhys Newbury, Jun Kwan, Valerio Ortenzi, Peter Corke, Manfred Grafinger

In experiments with 13 objects, the robot was able to successfully take the object from the human in 81. 9% of the trials.

Object Segmentation

Supportive Actions for Manipulation in Human-Robot Coworker Teams

no code implementations2 May 2020 Shray Bansal, Rhys Newbury, Wesley Chan, Akansel Cosgun, Aimee Allen, Dana Kulić, Tom Drummond, Charles Isbell

We compare two robot modes in a shared table pick-and-place task: (1) Task-oriented: the robot only takes actions to further its own task objective and (2) Supportive: the robot sometimes prefers supportive actions to task-oriented ones when they reduce future goal-conflicts.

Collaborative Planning for Mixed-Autonomy Lane Merging

no code implementations7 Aug 2018 Shray Bansal, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura

Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.

Decision Making

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations

no code implementations1 Jun 2018 Priyam Parashar, Akansel Cosgun, Alireza Nakhaei, Kikuo Fujimura

This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios.

Decision Making

Selective Experience Replay for Lifelong Learning

1 code implementation28 Feb 2018 David Isele, Akansel Cosgun

Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks, however deep nets typically exhibit forgetting when learning multiple tasks in sequence.

Transferring Autonomous Driving Knowledge on Simulated and Real Intersections

no code implementations30 Nov 2017 David Isele, Akansel Cosgun

We view intersection handling on autonomous vehicles as a reinforcement learning problem, and study its behavior in a transfer learning setting.

Autonomous Driving reinforcement-learning +2

Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

no code implementations2 May 2017 Akansel Cosgun, Lichao Ma, Jimmy Chiu, Jiawei Huang, Mahmut Demir, Alexandre Miranda Anon, Thang Lian, Hasan Tafish, Samir Al-Stouhi

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss.

Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously Handling Multiple Intersections

no code implementations2 May 2017 David Isele, Akansel Cosgun, Kikuo Fujimura

We analyze how the knowledge to autonomously handle one type of intersection, represented as a Deep Q-Network, translates to other types of intersections (tasks).

Transfer Learning

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

no code implementations2 May 2017 David Isele, Reza Rahimi, Akansel Cosgun, Kaushik Subramanian, Kikuo Fujimura

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers.

Autonomous Vehicles Navigate +2

Belief State Planning for Autonomously Navigating Urban Intersections

no code implementations14 Apr 2017 Maxime Bouton, Akansel Cosgun, Mykel J. Kochenderfer

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty.

Robotics

Cannot find the paper you are looking for? You can Submit a new open access paper.