SPN is a CNN that detects a spacecraft bounding box, classifies then regresses attitude, and optimizes position via Gauss-Newton, achieving degree-level attitude and cm-level position errors on real images after training only on synthetic data.
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
9 Pith papers cite this work. Polarity classification is still indexing.
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A reinforcement learning framework for AI coaching, modeled as a non-cooperative game with causal skill models, shows improved human learning outcomes in a drone racing user study over baselines.
HORIZON is a recoverability-governed checkpointed frontier curriculum for on-policy physical-domain scaling on quadruped locomotion that identifies three regularities: uneven widening, non-monotonic composition, and the necessity of joint on-policy interaction.
Presents MT-Libero, a GPU-parallel multi-task RL benchmark in Isaac Lab, and DGPO, an on-policy method combining importance-weighted PPO with adaptive behavior cloning from demonstrations.
Demo2Reward optimizes VLM reward model language instructions at test time from a few demonstrations to reduce false positives and enable policy learning in simulated and real robotic tasks without manual reward design.
Robots detect underspecified reward features via demonstration variation and query targeted natural language explanations to improve reward recovery from imperfect demos.
Robots discover causal tool features through VLM suggestions and physics-based counterfactual perturbations in simulation, then transfer manipulation skills via conditioned keypoint matching.
SynManDex generates human-like dexterous grasps for robots from synthetic human pre-grasps via retargeting and force-closure optimization, reporting 86.4% stability, 4.67/5 human-likeness, 80.7% sim success, and 83.3% real-robot success.
A lightweight RL framework trains terrain-agnostic 3D foothold-tracking policies for humanoids that transfer directly to real-world use as standalone low-level controllers.
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From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Demo2Reward optimizes VLM reward model language instructions at test time from a few demonstrations to reduce false positives and enable policy learning in simulated and real robotic tasks without manual reward design.