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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4 Pith papers cite this work. Polarity classification is still indexing.
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Isaac Gym achieves 2-3 orders of magnitude faster robot policy training by keeping physics simulation and PyTorch-based RL entirely on GPU with direct buffer sharing.
A comprehensive benchmark study of offline imitation learning methods on multi-stage robot manipulation tasks identifies key sensitivities to algorithm design, data quality, and stopping criteria while releasing all datasets and code.
MTG-Causal-RL is a new benchmark for causal RL using Magic: The Gathering with an explicit SCM, five archetypes, and CGFA-PPO agent showing competitive win rates plus diagnostic metrics.
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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Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym achieves 2-3 orders of magnitude faster robot policy training by keeping physics simulation and PyTorch-based RL entirely on GPU with direct buffer sharing.
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What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
A comprehensive benchmark study of offline imitation learning methods on multi-stage robot manipulation tasks identifies key sensitivities to algorithm design, data quality, and stopping criteria while releasing all datasets and code.
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Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
MTG-Causal-RL is a new benchmark for causal RL using Magic: The Gathering with an explicit SCM, five archetypes, and CGFA-PPO agent showing competitive win rates plus diagnostic metrics.