MPC-Injection biases off-policy RL locomotion policies toward controller-induced behavior basins by injecting MPC transitions into the replay buffer.
Towards bridging the space domain gap for satellite pose estimation using event sensing
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QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
Decoder-only transformer generates mechanisms from VAE latents of target curves, reporting mean Chamfer distance 0.0132 on held-out tests while avoiding explicit dataset lookup.
NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
OmniAct framework integrates planning, memory, and verification to enable persistent autonomy in omnimodal embodied agents, showing improved success and stable context in 40 real-world tasks.
citing papers explorer
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MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins
MPC-Injection biases off-policy RL locomotion policies toward controller-induced behavior basins by injecting MPC transitions into the replay buffer.
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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
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Discrete Autoregressive Transformer for Generative Mechanism Synthesis
Decoder-only transformer generates mechanisms from VAE latents of target curves, reporting mean Chamfer distance 0.0132 on held-out tests while avoiding explicit dataset lookup.
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CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
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Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
OmniAct framework integrates planning, memory, and verification to enable persistent autonomy in omnimodal embodied agents, showing improved success and stable context in 40 real-world tasks.