LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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RoboTwin 2.0 automates diverse synthetic data creation for dual-arm robots via MLLMs and five-axis domain randomization, leading to 228-367% gains in manipulation success.
RIPT-VLA applies RL with dynamic rollout sampling and leave-one-out advantage estimation to fine-tune VLA models, achieving up to 97.5% success rates and recovering from 4% to 97% success with one demonstration in 15 iterations.
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
citing papers explorer
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LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
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RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
RoboTwin 2.0 automates diverse synthetic data creation for dual-arm robots via MLLMs and five-axis domain randomization, leading to 228-367% gains in manipulation success.
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Interactive Post-Training for Vision-Language-Action Models
RIPT-VLA applies RL with dynamic rollout sampling and leave-one-out advantage estimation to fine-tune VLA models, achieving up to 97.5% success rates and recovering from 4% to 97% success with one demonstration in 15 iterations.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.