Obstacle Tower is a high fidelity, 3D, 3rd person, procedurally generated environment for reinforcement learning. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent’s ability to perform well on unseen instances of the environment.
19 PAPERS • 6 BENCHMARKS
BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab. This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located. The frame rate is 10fps/sec for RGB data and 3D point cloud. The dataset contains fully annotated frames which yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime).
10 PAPERS • NO BENCHMARKS YET
The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. This ability is referred to as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. PackIt is a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space.
3 PAPERS • 1 BENCHMARK