ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
Learning agile robotic locomotion skills by imitating animals
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A single-qubit quantum reinforcement learning agent solves CartPole faster than classical networks and quantifies shot-count versus control-frequency requirements for real-time closed-loop control on NISQ hardware, including direct electronics programming to reduce latency.
DynaRetarget refines human kinematic motions into dynamically feasible humanoid trajectories using incremental sampling-based trajectory optimization, achieving higher success rates than prior methods on diverse object interaction tasks.
citing papers explorer
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Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
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Towards Real-time Control of a CartPole System on a Quantum Computer
A single-qubit quantum reinforcement learning agent solves CartPole faster than classical networks and quantifies shot-count versus control-frequency requirements for real-time closed-loop control on NISQ hardware, including direct electronics programming to reduce latency.
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DynaRetarget: Dynamically-Feasible Retargeting using Sampling-Based Trajectory Optimization
DynaRetarget refines human kinematic motions into dynamically feasible humanoid trajectories using incremental sampling-based trajectory optimization, achieving higher success rates than prior methods on diverse object interaction tasks.