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.
End-to-end training of deep visuomotor policies
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
MSACT improves localization stability and task success rates in limited-data bimanual manipulation by extracting stable 2D attention points and aligning predicted attention sequences across frames without keypoint labels.
SEGP-VAE learns stable low-dimensional LTI systems from video data by deriving GP mean and covariance from LTI equations and using a complete unconstrained parametrization of semi-contracting systems.
AnyUser translates free-form sketches on images plus optional language into executable robot actions for domestic tasks using multimodal fusion and a hierarchical policy.
citing papers explorer
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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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MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation
MSACT improves localization stability and task success rates in limited-data bimanual manipulation by extracting stable 2D attention points and aligning predicted attention sequences across frames without keypoint labels.
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Stability Enhanced Gaussian Process Variational Autoencoders
SEGP-VAE learns stable low-dimensional LTI systems from video data by deriving GP mean and covariance from LTI equations and using a complete unconstrained parametrization of semi-contracting systems.
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AnyUser: Translating Sketched User Intent into Domestic Robots
AnyUser translates free-form sketches on images plus optional language into executable robot actions for domestic tasks using multimodal fusion and a hierarchical policy.
- Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning