A multi-agent RL high-level planner outputs task-space velocities that a GPU-parallel QP low-level controller converts to joint velocities while enforcing limits and collisions, yielding robust sim-to-real dexterous grasping with zero-shot steerability.
Cusadi: A gpu parallelization framework for symbolic expressions and optimal control
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GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
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Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
A multi-agent RL high-level planner outputs task-space velocities that a GPU-parallel QP low-level controller converts to joint velocities while enforcing limits and collisions, yielding robust sim-to-real dexterous grasping with zero-shot steerability.
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GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.