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Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation
Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP)
have demonstrated impressive performance across various robotic manipulation tasks …
have demonstrated impressive performance across various robotic manipulation tasks …
Robel: Robotics benchmarks for learning with low-cost robots
ROBEL is an open-source platform of cost-effective robots designed for reinforcement
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …
learning in the real world. ROBEL introduces two robots, each aimed to accelerate …
Trifinger: An open-source robot for learning dexterity
M Wüthrich, F Widmaier, F Grimminger, J Akpo… - ar**
Robot learning is widely accepted by academia and industry with its potentials to transform
autonomous robot control through machine learning. Inspired by widely used soft fingers on …
autonomous robot control through machine learning. Inspired by widely used soft fingers on …
Robotic manipulation datasets for offline compositional reinforcement learning
Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train
on large datasets, avoiding the recurrence of expensive data collection. To advance the …
on large datasets, avoiding the recurrence of expensive data collection. To advance the …
Reinforcement learning experiments and benchmark for solving robotic reaching tasks
Reinforcement learning has shown great promise in robotics thanks to its ability to develop
efficient robotic control procedures through self-training. In particular, reinforcement learning …
efficient robotic control procedures through self-training. In particular, reinforcement learning …