E²DT couples a Decision Transformer with a k-Determinantal Point Process that scores trajectories on return-to-go quantiles, predictive uncertainty, and stage coverage to improve sample efficiency and policy quality in robotic manipulation.
Trends and challenges in robot manipula- tion
4 Pith papers cite this work. Polarity classification is still indexing.
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A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
GraspSense computes force maps from object geometry to select mechanically safe grasp regions and regulate grip forces for dexterous hands.
A hybrid visual-motor imagery EEG decoder controls a robot for grasping and placement at 40% and 63% accuracy respectively, yielding 21% end-to-end task success in cue-free online use.
citing papers explorer
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E$^2$DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
E²DT couples a Decision Transformer with a k-Determinantal Point Process that scores trajectories on return-to-go quantiles, predictive uncertainty, and stage coverage to improve sample efficiency and policy quality in robotic manipulation.
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Towards Robotic Dexterous Hand Intelligence: A Survey
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
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GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps
GraspSense computes force maps from object geometry to select mechanically safe grasp regions and regulate grip forces for dexterous hands.
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Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery
A hybrid visual-motor imagery EEG decoder controls a robot for grasping and placement at 40% and 63% accuracy respectively, yielding 21% end-to-end task success in cue-free online use.