MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption

arXiv:2609.21122 · cs.RO · Submitted 2026-09-17 · Read on arXiv

cs.RO

Submitted: 2026-09-17

Updated: 2026-09-17

Project page: https://hacman-2023.github.io

Terminology

• Adaptation — 33× in this paper · explained in 1 episode
• Meta-learning — 24× in this paper · explained in 2 episodes
• Adaptive Planning — 18× in this paper · explained in 1 episode
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Active Learning — 8× in this paper · explained in 6 episodes
• Online Adaptation — 6× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Actor — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 3× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Actor-Critic Model — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hypernetwork — 1× in this paper · explained in 3 episodes
• Model-Agnostic Meta-Learning — 1× in this paper
• Motion Primitives — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• ${\tt KRAFT}$: Sampling-Based Kinodynamic Replanning and Feedback Control over Approximate, Identified Models of Vehicular Systems
• AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
• Fast Asymptotically Optimal Kinodynamic Planning via Vectorization

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