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Found 8 papers, 2 papers with code
Pick Planning Strategies for Large-Scale Package Manipulation
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.
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success
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.
Passing Through Narrow Gaps with Deep Reinforcement Learning
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.
Learning Setup Policies: Reliable Transition Between Locomotion Behaviours
Dynamic platforms that operate over many unique terrain conditions typically require many behaviours.
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ.
Learning When to Switch: Composing Controllers to Traverse a Sequence of Terrain Artifacts
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
Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning.
Deep Leaf Segmentation Using Synthetic Data
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).
