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
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7representative citing papers
X-Morph retargets human motions to kinematically plausible references for multiple legged morphologies, trains privileged RL trackers, and distills them into deployable policies that generalize and enable teleoperation and text-conditioned generation.
A three-stage RL framework with cross-attention, spatial memory, and depth-noise augmentation lets a Unitree Go2 climb 55° hollow stairs zero-shot from simulation.
Adversarial Posture Regularization matches RL policy posture distributions to casual human piano-playing data to enforce human-like kinematics in dexterous hands, outperforming baselines on cPSI, BSE, and FAC metrics.
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's SBTO, which incrementally extends the optimization horizon while warm-starting from shorter solutions, refines kinematic humanoid demonstrations into dynamically consistent whole-body motions with higher success than receding-horizon sampling MPC.
Framework using parameterized Signal Temporal Logic specifications to shape rewards for PPO-based RL, yielding tighter velocity tracking and more stable training than hand-crafted rewards on Barkour quadruped in MuJoCo simulation.
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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X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies
X-Morph retargets human motions to kinematically plausible references for multiple legged morphologies, trains privileged RL trackers, and distills them into deployable policies that generalize and enable teleoperation and text-conditioned generation.
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StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots
A three-stage RL framework with cross-attention, spatial memory, and depth-noise augmentation lets a Unitree Go2 climb 55° hollow stairs zero-shot from simulation.
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Enforcing Human-like Kinematics in Dexterous Piano Playing via Adversarial Posture Regularization
Adversarial Posture Regularization matches RL policy posture distributions to casual human piano-playing data to enforce human-like kinematics in dexterous hands, outperforming baselines on cPSI, BSE, and FAC metrics.
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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's SBTO, which incrementally extends the optimization horizon while warm-starting from shorter solutions, refines kinematic humanoid demonstrations into dynamically consistent whole-body motions with higher success than receding-horizon sampling MPC.
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Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
Framework using parameterized Signal Temporal Logic specifications to shape rewards for PPO-based RL, yielding tighter velocity tracking and more stable training than hand-crafted rewards on Barkour quadruped in MuJoCo simulation.