RLBench
A widely used simulation benchmark platform in robot learning, built on CoppeliaSim. It provides more than 100 manipulation tasks (such as pick-and-place, opening doors and peg insertion), each with expert demonstrations and dense rewards.
Timeline (last 90 days)
According to data published by the HexaShell project team, the VLM reached a 78% success rate within three attempts on the RLBench pick-and-place task.
Ablation experiments show that inserting a learned motion planner between the VLM and the robot raises the RLBench success rate to about 90%.
Current benchmarks (RLBench and Meta-World) lean toward single-arm fixed-base setups, whereas industrial production lines often use 7-DOF arms or mobile bases, so conclusions should be transferred with caution.
RLBench is a simulation benchmark platform built on CoppeliaSim, offering more than 100 manipulation tasks and supporting multi-view RGB-D observations and a standardized evaluation protocol.
Meta-World provides 50 tabletop manipulation tasks of varying difficulty, RLBench includes 100 tasks and supports multiple observation modes, and CALVIN focuses on language-conditioned long-horizon manipulation task chains — these are the main simulation evaluation benchmarks in the field of robotic manipulation.
Benchmark platforms such as Meta-World, RLBench, and CALVIN provide standardized multi-task evaluation environments.
All Facts (6)
According to data published by the HexaShell project team, the VLM reached a 78% success rate within three attempts on the RLBench pick-and-place task.
50%UnverifiedAblation experiments show that inserting a learned motion planner between the VLM and the robot raises the RLBench success rate to about 90%.
50%UnverifiedCurrent benchmarks (RLBench and Meta-World) lean toward single-arm fixed-base setups, whereas industrial production lines often use 7-DOF arms or mobile bases, so conclusions should be transferred with caution.
50%UnverifiedRLBench is a simulation benchmark platform built on CoppeliaSim, offering more than 100 manipulation tasks and supporting multi-view RGB-D observations and a standardized evaluation protocol.
50%UnverifiedMeta-World provides 50 tabletop manipulation tasks of varying difficulty, RLBench includes 100 tasks and supports multiple observation modes, and CALVIN focuses on language-conditioned long-horizon manipulation task chains — these are the main simulation evaluation benchmarks in the field of robotic manipulation.
50%UnverifiedBenchmark platforms such as Meta-World, RLBench, and CALVIN provide standardized multi-task evaluation environments.
50%