no code implementations • 23 Sep 2023 • Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Sicong Zhao, Charles Swan, Kostas Bekris
Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations.
no code implementations • 17 May 2023 • Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Charles Swan, Kostas Bekris
This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which utilizes a pick success predictor trained on real production data.
no code implementations • 6 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.
no code implementations • 23 Jan 2021 • Brendan Tidd, Nicolas Hudson, Akansel Cosgun, Jurgen Leitner
Dynamic platforms that operate over many unique terrain conditions typically require many behaviours.
1 code implementation • 24 Nov 2020 • Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege, David Howard, Nicolas Hudson
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ.
no code implementations • 1 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.
no code implementations • 8 Oct 2020 • Brendan Tidd, Nicolas Hudson, Akansel Cosgun
Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning.
3 code implementations • 28 Jul 2018 • Daniel Ward, Peyman Moghadam, Nicolas Hudson
Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC).