Scouting the Dynamics Gap: Test-Time Policy Adaptation via Action-Outcome Feedback
cs.RO
Submitted: 2026-09-28
Updated: 2026-09-28
Comments: Accepted to Conference of Robot Learning (CoRL) 2026
Project page: https://liy1shu.github.io/SCOUT
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Tent: Fully Test-time Adaptation by Entropy Minimization
- On First-Order Meta-Learning Algorithms
- RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
- Learning to reinforcement learn
- Self-Supervised Policy Adaptation during Deployment
- Preparing for the Unknown: Learning a Universal Policy with Online System Identification
- FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
- World Models
- Dream to Control: Learning Behaviors by Latent Imagination
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
- Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
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