EgoPush: Egocentric Multi-Object Rearrangement for Mobile Robots via Constrained Teacher Observability

arXiv:2602.18071 · cs.RO · Submitted 2026-02-20 · Read on arXiv

cs.RO

Submitted: 2026-02-20

Updated: 2026-09-18

Project page: https://ai4ce.github.io/EgoPush

Terminology

• Distillation — 23× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Reinforcement Learning — 13× in this paper · explained in 4 episodes
• Credit assignment — 12× in this paper · explained in 3 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Ablation Studies — 6× in this paper · explained in 1 episode
• Latent Space — 5× in this paper · explained in 7 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Active Perception — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• Sample Efficiency — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• International Conference on Robotics and Automation — 3× in this paper
• Sim-to-Real Transfer — 3× in this paper · explained in 2 episodes
• Spatial Intention Maps — 3× in this paper
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Online Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 2× in this paper
• Reward Function — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Camera Depth Model — 1× in this paper
• Cooperation — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In Conference on Robot Learning — 1× in this paper
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 5 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Teacher Success Rate — 1× in this paper
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Push Anything: Single- and Multi-Object Pushing From First Sight with Contact-Implicit MPC
• Interactive Navigation with Adaptive Non-prehensile Mobile Manipulation
• Humanoid Parkour Learning
• VisualMimic: Visual Humanoid Loco-Manipulation via Motion Tracking and Generation
• Accelerating Visual-Policy Learning through Parallel Differentiable Simulation
• High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning

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