LIBERO-Pro
A benchmark for evaluating robotic manipulation policies, used to test policy robustness in out-of-distribution scenarios such as positional perturbations and instruction perturbations.
Timeline (last 90 days)
On the LIBERO-Pro benchmark, after 15 rounds of generation and training, CoDA raised the success rate under position perturbation from 25% to 84%, and under instruction perturbation from 1% to 76%.
ChunkAct improves task success rate on the LIBERO-Pro benchmark by 5.0 percentage points over the current state-of-the-art method in the agentic robotics field
ChunkAct reduces the number of LLM calls by 2.9x on LIBERO-Pro
ChunkAct reduces input tokens by 4.4x and output tokens by 2.3x on LIBERO-Pro
The LIBERO benchmark contains four task suites: Spatial, Object, Goal, and Long; LIBERO-Pro is its enhanced version, increasing task complexity and the number of steps
All Facts (5)
On the LIBERO-Pro benchmark, after 15 rounds of generation and training, CoDA raised the success rate under position perturbation from 25% to 84%, and under instruction perturbation from 1% to 76%.
50%UnverifiedChunkAct improves task success rate on the LIBERO-Pro benchmark by 5.0 percentage points over the current state-of-the-art method in the agentic robotics field
50%UnverifiedChunkAct reduces the number of LLM calls by 2.9x on LIBERO-Pro
50%UnverifiedChunkAct reduces input tokens by 4.4x and output tokens by 2.3x on LIBERO-Pro
50%UnverifiedThe LIBERO benchmark contains four task suites: Spatial, Object, Goal, and Long; LIBERO-Pro is its enhanced version, increasing task complexity and the number of steps
50%